You are provided with a research question, keyphrases about the question, a description of candidate datasets and dataset options. Read the description of popular datasets provided below and select the ones that can be used to validate the following research question. Use your knowledge of machine learning datasets to make the best judgement.
Your response should be formatted as a `json` array. For instance, for the query "Semi supervised image classification", a sample response might be: ["CIFAR-10", "CIFAR-100"]. Do not include any extra text in the response other than the answer array.

Query: A method for model compression that can prune deep neural networks in one go without requiring any degree of

Keyphrases: model compression video

Dataset description:
DuoRC: DuoRC contains 186,089 unique question-answer pairs created from a collection of 7680 pairs of movie plots where each pair in the collection reflects two versions of the same movie. Why another RC dataset?
METU Trademark: The METU Trademark Dataset is a large dataset (the largest publicly available logo dataset as of 2014, and the largest one not requiring any preprocessing as of 2017), which is composed of more than 900K real logos belonging to real companies worldwide. The dataset also includes query sets of varying difficulties, allowing Trademark Retrieval researchers to benchmark their methods against other methods to progress the field.
COCO: The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.
ICB: A carefully chosen set of high-resolution high-precision natural images suited for compression algorithm evaluation. The images historically used for compression research (lena, barbra, pepper etc...) have outlived their useful life and its about time they become a part of history only.
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).
UNITOPATHO: Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma. Colorectal polyps characterization relies on the histological analysis of tissue samples to determine the polyps malignancy and dysplasia grade.

Options:- A: DuoRC  B: METU Trademark  C: COCO  D: ICB  E: CIFAR-10  F: UNITOPATHO
["COCO", "CIFAR-10"]