Add task category, paper, project page, and code links, and update citation

This PR significantly enhances the MM-OPERA dataset card by:
- Adding `image-text-to-text` to the `task_categories` metadata, improving discoverability for relevant research.
- Including direct links to the associated paper ([https://huggingface.co/papers/2510.26937](https://huggingface.co/papers/2510.26937)), the project page ([https://mm-opera-bench.github.io/](https://mm-opera-bench.github.io/)), and the GitHub repository ([https://github.com/MM-OPERA-Bench/MM-OPERA](https://github.com/MM-OPERA-Bench/MM-OPERA)) at the top of the README.
- Updating the citation BibTeX to reflect the NeurIPS 2025 publication, as indicated in the project's GitHub repository.

These changes provide a more comprehensive and accessible resource for researchers utilizing the MM-OPERA dataset.

README.md

@@ -1,6 +1,9 @@
 ---
+license: cc-by-4.0
 tags:
 - Multimodal
+task_categories:
+- image-text-to-text
 dataset_info:
   features:
   - name: id
@@ -58,8 +61,10 @@ dataset_info:
     dtype: string
   - name: img_id3
     dtype: string
+    split: ica
   - name: filename3
     dtype: string
+    split: ica
   - name: description3
     dtype: string
     split: ica
@@ -85,11 +90,12 @@ configs:
     path: data/ria-*
   - split: ica
     path: data/ica-*
-license: cc-by-4.0
 ---
 
 # MM-OPERA: Multi-Modal OPen-Ended Reasoning-guided Association Benchmark 🧠🌐
 
+[Paper](https://huggingface.co/papers/2510.26937) | [Project Page](https://mm-opera-bench.github.io/) | [Code](https://github.com/MM-OPERA-Bench/MM-OPERA)
+
 ## Overview 📖
 
 MM-OPERA is a benchmark designed to evaluate the open-ended association reasoning capabilities of Large Vision-Language Models (LVLMs). With 11,497 instances, it challenges models to identify and express meaningful connections across distant concepts in an open-ended format, mirroring human-like reasoning. The dataset spans diverse cultural, linguistic, and thematic contexts, making it a robust tool for advancing multimodal AI research. 🌍✨
@@ -163,14 +169,10 @@ Explore MM-OPERA to unlock the next level of multimodal association reasoning!
 If you use this dataset in your work, please cite it as follows:
 
 ```bibtex
-@misc{huang2025mmopera,
-  author    = {Zimeng Huang and Jinxin Ke and Xiaoxuan Fan and Yufeng Yang and Yang Liu and Liu Zhonghan and Zedi Wang and Junteng Dai and Haoyi Jiang and Yuyu Zhou and Keze Wang and Ziliang Chen},
-  title     = {MM-OPERA},
-  month     = {oct},
-  year      = {2025},
-  publisher = {Zenodo},
-  version   = {1.0.0},
-  doi       = {10.5281/zenodo.17300924},
-  url       = {https://doi.org/10.5281/zenodo.17300924}
+@inproceedings{huang2025mmopera,
+  title={{MM-OPERA: Benchmarking Open-ended Association Reasoning for Large Vision-Language Models}},
+  author={Zimeng Huang and Jinxin Ke and Xiaoxuan Fan and Yufeng Yang and Yang Liu and Liu Zhonghan and Zedi Wang and Junteng Dai and Haoyi Jiang and Yuyu Zhou and Keze Wang and Ziliang Chen},
+  booktitle={Advances in Neural Information Processing Systems 39},
+  year={2025}
 }
-```
+```