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: We present a novel deep self-paced learning algorithm for person re-identification. Specifically,

Keyphrases: person re-id images

Dataset description:
Partial-iLIDS: Partial iLIDS is a dataset for occluded person person re-identification. It contains a total of 476 images of 119 people captured by 4 non-overlapping cameras.
SYSU-MM01: The SYSU-MM01 is a dataset collected for the Visible-Infrared Re-identification problem. The images in the dataset were obtained from 491 different persons by recording them using 4 RGB and 2 infrared cameras.
CUHK-PEDES: The CUHK-PEDES dataset is a caption-annotated pedestrian dataset. It contains 40,206 images over 13,003 persons.
Airport: The Airport dataset is a dataset for person re-identification which consists of 39,902 images and 9,651 identities across six cameras.
CUHK03: The CUHK03 consists of 14,097 images of 1,467 different identities, where 6 campus cameras were deployed for image collection and each identity is captured by 2 campus cameras. This dataset provides two types of annotations, one by manually labelled bounding boxes and the other by bounding boxes produced by an automatic detector.
VIPeR: The Viewpoint Invariant Pedestrian Recognition (VIPeR) dataset includes 632 people and two outdoor cameras under different viewpoints and light conditions. Each person has one image per camera and each image has been scaled to be 128×48 pixels.

Options:- A: Partial-iLIDS  B: SYSU-MM01  C: CUHK-PEDES  D: Airport  E: CUHK03  F: VIPeR
["CUHK03", "VIPeR"]