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 train a zero-shot video classification model that can generalize to a new task

Keyphrases: zero-shot video classification

Dataset description:
RareAct: RareAct is a video dataset of unusual actions, including actions like “blend phone”, “cut keyboard” and “microwave shoes”. It aims at evaluating the zero-shot and few-shot compositionality of action recognition models for unlikely compositions of common action verbs and object nouns.
FewRel 2.0: A more challenging task to investigate two aspects of few-shot relation classification models: (1) Can they adapt to a new domain with only a handful of instances? (2) Can they detect none-of-the-above (NOTA) relations?
ActivityNet: The ActivityNet dataset contains 200 different types of activities and a total of 849 hours of videos collected from YouTube. ActivityNet is the largest benchmark for temporal activity detection to date in terms of both the number of activity categories and number of videos, making the task particularly challenging.
UCF101: UCF101 dataset is an extension of UCF50 and consists of 13,320 video clips, which are classified into 101 categories. These 101 categories can be classified into 5 types (Body motion, Human-human interactions, Human-object interactions, Playing musical instruments and Sports).
KILT: KILT (Knowledge Intensive Language Tasks) is a benchmark consisting of 11 datasets representing 5 types of tasks:   Fact-checking (FEVER), Entity linking (AIDA CoNLL-YAGO, WNED-WIKI, WNED-CWEB), Slot filling (T-Rex, Zero Shot RE), Open domain QA (Natural Questions, HotpotQA, TriviaQA, ELI5), Dialog generation (Wizard of Wikipedia). All these datasets have been grounded in a single pre-processed wikipedia snapshot, allowing for fairer and more consistent evaluation as well as enabling new task setups such as multitask and transfer learning.

Options:- A: RareAct  B: FewRel 2.0  C: ActivityNet  D: UCF101  E: KILT
["ActivityNet", "UCF101"]