Given a short query with keyphrases about a research idea in 2017 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.

Query: A pooling method for human action recognition in video.

Keyphrases: action recognition video

Dataset description:
Kinetics-600: The Kinetics-600 is a large-scale action recognition dataset which consists of around 480K videos from 600 action categories. The 480K videos are divided into 390K, 30K, 60K for training, validation and test sets, respectively.
HAA500: HAA500 is a manually annotated human-centric atomic action dataset for action recognition on 500 classes with over 591k labeled frames. Unlike existing atomic action datasets, where coarse-grained atomic actions were labeled with action-verbs, e.g., "Throw", HAA500 contains fine-grained atomic actions where only consistent actions fall under the same label, e.g., "Baseball Pitching" vs "Free Throw in Basketball", to minimize ambiguities in action classification.
HMDB51: The HMDB51 dataset is a large collection of realistic videos from various sources, including movies and web videos. The dataset is composed of 6,766 video clips from 51 action categories (such as “jump”, “kiss” and “laugh”), with each category containing at least 101 clips.
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).

Options:- A: Kinetics-600  B: HAA500  C: HMDB51  D: UCF101
UCF101