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: We propose a simple but effective postprocessing method to render off-the-shelf feature representations even stronger

Keyphrases: image recognition

Dataset description:
MERL Shopping: MERL Shopping is a dataset for training and testing action detection algorithms. The MERL Shopping Dataset consists of 106 videos, each of which is a sequence about 2 minutes long.
CIFAR-10: The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck).
K-EmoCon: A multimodal dataset with comprehensive annotations of continuous emotions during naturalistic conversations. The dataset contains multimodal measurements, including audiovisual recordings, EEG, and peripheral physiological signals, acquired with off-the-shelf devices from 16 sessions of approximately 10-minute long paired debates on a social issue.
Griddly: Griddly is an environment for grid-world based research. Griddly provides a highly optimized game state and rendering engine with a flexible high-level interface for configuring environments.

Options:- A: MERL Shopping  B: CIFAR-10  C: K-EmoCon  D: Griddly
CIFAR-10