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: This paper studies the problem of natural language inference (NLI) from the distributional robustness

Keyphrases: natural language inference text

Dataset description:
CelebA: CelebFaces Attributes dataset contains 202,599 face images of the size 178×218 from 10,177 celebrities, each annotated with 40 binary labels indicating facial attributes like hair color, gender and age.
HELP: The HELP dataset is an automatically created natural language inference (NLI) dataset that embodies the combination of lexical and logical inferences focusing on monotonicity (i.e., phrase replacement-based reasoning). The HELP (Ver.1.0) has 36K inference pairs consisting of upward monotone, downward monotone, non-monotone, conjunction, and disjunction.
Places: The Places dataset is proposed for scene recognition and contains more than 2.5 million images covering more than 205 scene categories with more than 5,000 images per category.
IMPPRES: An IMPlicature and PRESupposition diagnostic dataset (IMPPRES), consisting of >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types.
MultiNLI: The Multi-Genre Natural Language Inference (MultiNLI) dataset has 433K sentence pairs. Its size and mode of collection are modeled closely like SNLI.

Options:- A: CelebA  B: HELP  C: Places  D: IMPPRES  E: MultiNLI
["CelebA", "Places", "MultiNLI"]