Given a short query with keyphrases about a research idea in 2020 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: The process of transfer learning aims to find an effective way to transfer knowledge from a source dataset to

Keyphrases: transfer learning image classification

Dataset description:
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).
MetaLWOz: Collected by leveraging background knowledge from a larger, more highly represented dialogue source.
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.
KdConv: KdConv is a Chinese multi-domain Knowledge-driven Conversation dataset, grounding the topics in multi-turn conversations to knowledge graphs. KdConv contains 4.5K conversations from three domains (film, music, and travel), and 86K utterances with an average turn number of 19.0.

Options:- A: CIFAR-10  B: MetaLWOz  C: KILT  D: KdConv
CIFAR-10