You are provided with a research question, keyphrases about the question, a description of candidate datasets and dataset options. Read the description of popular datasets provided below and select the ones that can be used to validate the following research question. Use your knowledge of machine learning datasets to make the best judgement.
Your response should be formatted as a `json` array. For instance, for the query "Semi supervised image classification", a sample response might be: ["CIFAR-10", "CIFAR-100"]. Do not include any extra text in the response other than the answer array.

Query: In this paper, we discuss the challenges and state-of-the-art methods

Keyphrases: crowd counting density estimation images

Dataset description:
THEODORE: Recent work about synthetic indoor datasets from perspective views has shown significant improvements of object detection results with Convolutional Neural Networks(CNNs). In this paper, we introduce THEODORE: a novel, large-scale indoor dataset containing 100,000 high- resolution diversified fisheye images with 14 classes.
HellaSwag: HellaSwag is a challenge dataset for evaluating commonsense NLI that is specially hard for state-of-the-art models, though its questions are trivial for humans (>95% accuracy).
Completion3D: The Completion3D benchmark is a dataset for evaluating state-of-the-art 3D Object Point Cloud Completion methods. Ggiven a partial 3D object point cloud the goal is to infer a complete 3D point cloud for the object.
Mall: The Mall is a dataset for crowd counting and profiling research. Its images are collected from publicly accessible webcam.
ShanghaiTech: The Shanghaitech dataset is a large-scale crowd counting dataset. It consists of 1198 annotated crowd images.

Options:- A: THEODORE  B: HellaSwag  C: Completion3D  D: Mall  E: ShanghaiTech
["Mall", "ShanghaiTech"]