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: I want to search images by using text queries.

Keyphrases: image-text search images texts paragraph-level

Dataset description:
COCO: The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.
QUASAR-T: QUASAR-T is a large-scale dataset aimed at evaluating systems designed to comprehend a natural language query and extract its answer from a large corpus of text. It consists of 43,013 open-domain trivia questions and their answers obtained from various internet sources.
QReCC: QReCC contains 14K conversations with 81K question-answer pairs. QReCC is built on questions from TREC CAsT, QuAC and Google Natural Questions.
MUSE: The MUSE dataset contains bilingual dictionaries for 110 pairs of languages. For each language pair, the training seed dictionaries contain approximately 5000 word pairs while the evaluation sets contain 1500 word pairs.
QUASAR-S: QUASAR-S is a large-scale dataset aimed at evaluating systems designed to comprehend a natural language query and extract its answer from a large corpus of text. It consists of 37,362 cloze-style (fill-in-the-gap) queries constructed from definitions of software entity tags on the popular website Stack Overflow.
SNIPS: The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:   SearchCreativeWork (e.g. Find me the I, Robot television show), GetWeather (e.g.

Options:- A: COCO  B: QUASAR-T  C: QReCC  D: MUSE  E: QUASAR-S  F: SNIPS
["COCO", "MUSE"]