Given a short query with keyphrases about a research idea in 2019 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: I want to understand how spatiotemporal representations are learned in two-stream networks for action recognition.

Keyphrases: action recognition video

Dataset description:
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).
NAB: The First Temporal Benchmark Designed to Evaluate  Real-time Anomaly Detectors Benchmark  The growth of the Internet of Things has created an abundance of streaming data. Finding anomalies in this data can provide valuable insights into opportunities or failures.
FineGym: FineGym is an action recognition dataset build on top of gymnasium videos. Compared to existing action recognition datasets, FineGym is distinguished in richness, quality, and diversity.
SONYC-UST-V2: A dataset for urban sound tagging with spatiotemporal information. This dataset is aimed for the development and evaluation of machine listening systems for real-world urban noise monitoring.

Options:- A: UCF101  B: NAB  C: FineGym  D: SONYC-UST-V2
UCF101