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 exposition, we extensively compare 30+ state-of-the-art super-

Keyphrases: super-resolution image

Dataset description:
Set5: The Set5 dataset is a dataset consisting of 5 images (“baby”, “bird”, “butterfly”, “head”, “woman”) commonly used for testing performance of Image Super-Resolution models.
Dialogue State Tracking Challenge: The Dialog State Tracking Challenges 2 & 3 (DSTC2&3) were research challenge focused on improving the state of the art in tracking the state of spoken dialog systems. State tracking, sometimes called belief tracking, refers to accurately estimating the user's goal as a dialog progresses.
Urban100: The Urban100 dataset contains 100 images of urban scenes. It commonly used as a test set to evaluate the performance of super-resolution models.
FLIC: The FLIC dataset contains 5003 images from popular Hollywood movies. The images were obtained by running a state-of-the-art person detector on every tenth frame of 30 movies.
REDS: The realistic and dynamic scenes (REDS) dataset was proposed in the NTIRE19 Challenge. The dataset is composed of 300 video sequences with resolution of 720×1,280, and each video has 100 frames, where the training set, the validation set and the testing set have 240, 30 and 30 videos, respectively
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.
Talk2Car: The Talk2Car dataset finds itself at the intersection of various research domains, promoting the development of cross-disciplinary solutions for improving the state-of-the-art in grounding natural language into visual space. The annotations were gathered with the following aspects in mind: Free-form high quality natural language commands, that stimulate the development of solutions that can operate in the wild.

Options:- A: Set5  B: Dialogue State Tracking Challenge  C: Urban100  D: FLIC  E: REDS  F: THEODORE  G: Talk2Car
["Set5", "Urban100"]