human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "person re identification by contour sketch under moderate clothing change"):
  Abstract: The majority of existing models of person re-identification(re-id) that matches pedestrian images across different camera views are largely dependent on color appearance. However, this can be an issue if that person changes his/her clothes, causing most existing methods to fail. We call person re-id under clothing change the "cross-clothes person re-id". Particularly, as a first attempt at solving this problem using visible light images, we assume a person only changes his/her clothes moderately when the weather doesn't change substantially within a short period so that the shape of a person would not change significantly. We perform cross-clothes person re-id based on a contour sketch of person image to take advantage of the shape of human body instead of color information. To select more discriminative curve patterns on a body contour sketch, we introduce a learning-based spatial polar transformation(SPT) layer in convolutional neural network(CNN) to transform contour sketch images into a polar coordinate space. An angle-specific extractor(ASE) is applied in the CNN to extract more fine-grained discriminant angle-specific features. Finally, we develop a multistream network for aggregating multi-granularity features. We also contribute a cross-clothes dataset consisting of 33698 images from 221 identities. Our experiments demonstrate the effectiveness of our proposed method.

Includes members:
  Member "openpose realtime multi person 2d pose estimation using part affinity fields":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "harmonious attention network for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "attention aware compositional network for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification")
  Member "part aligned bilinear representations for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "surveillance face recognition challenge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fan feature adaptation network for surveillance face recognition and normalization")
  Member "polar transformer networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rsl net localising in satellite images from a radar on the ground")
  Member "deformable convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "hydraplus net attentive deep features for pedestrian analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification")

Question: Please tell me which class the source paper belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.CV(Computer Vision and Pattern Recognition)