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: This paper establishes a connection between feed-forward neural networks and probabilistic graphical models.

Keyphrases: bn structure learning images

Dataset description:
SVG-Icons8: A new large-scale dataset along with an open-source library for SVG manipulation.
PROTEINS: PROTEINS is a dataset of proteins that are classified as enzymes or non-enzymes. Nodes represent the amino acids and two nodes are connected by an edge if they are less than 6 Angstroms apart.
Decagon: Bio-decagon is a dataset for polypharmacy side effect identification problem framed as a multirelational link prediction problem in a two-layer multimodal graph/network of two node types: drugs and proteins. Protein-protein interaction network describes relationships between proteins.
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).

Options:- A: SVG-Icons8  B: PROTEINS  C: Decagon  D: CIFAR-10
CIFAR-10