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: This paper presents a novel joint head and human detection network, JointDet, which effectively detects

Keyphrases: head human detection images

Dataset description:
MLFP: The MLFP dataset consists of face presentation attacks captured with seven 3D latex masks and three 2D print attacks. The dataset contains videos captured from color, thermal and infrared channels.
WinoGrande: WinoGrande is a large-scale dataset of 44k problems, inspired by the original WSC design, but adjusted to improve both the scale and the hardness of the dataset. The key steps of the dataset construction consist of (1) a carefully designed crowdsourcing procedure, followed by (2) systematic bias reduction using a novel AfLite algorithm that generalizes human-detectable word associations to machine-detectable embedding associations.
CrowdHuman: CrowdHuman is a large and rich-annotated human detection dataset, which contains 15,000, 4,370 and 5,000 images collected from the Internet for training, validation and testing respectively. The number is more than 10× boosted compared with previous challenging pedestrian detection dataset like CityPersons.
SCUT-HEAD: Includes 4405 images with 111251 heads annotated.
CityPersons: The CityPersons dataset is a subset of Cityscapes which only consists of person annotations. There are 2975 images for training, 500 and 1575 images for validation and testing.

Options:- A: MLFP  B: WinoGrande  C: CrowdHuman  D: SCUT-HEAD  E: CityPersons
["CrowdHuman", "CityPersons"]