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 "kernel cross view collaborative representation based classification for person re identification"):
  Abstract: Person re-identification aims at the maintenance of a global identity as a person moves among non-overlapping surveillance cameras. It is a hard task due to different illumination conditions, viewpoints and the small number of annotated individuals from each pair of cameras (small-sample-size problem). Collaborative Representation based Classification (CRC) has been employed successfully to address the small-sample-size problem in computer vision. However, the original CRC formulation is not well-suited for person re-identification since it does not consider that probe and gallery samples are from different cameras. Furthermore, it is a linear model, while appearance changes caused by different camera conditions indicate a strong nonlinear transition between cameras. To overcome such limitations, we propose the Kernel Cross-View Collaborative Representation based Classification (Kernel X-CRC) that represents probe and gallery images by balancing representativeness and similarity nonlinearly. It assumes that a probe and its corresponding gallery image are represented with similar coding vectors using individuals from the training set. Experimental results demonstrate that our assumption is true when using a high-dimensional feature vector and becomes more compelling when dealing with a low-dimensional and discriminative representation computed using a common subspace learning method. We achieve state-of-the-art for rank-1 matching rates in two person re-identification datasets (PRID450S and GRID) and the second best results on VIPeR and CUHK01 datasets.

Includes members:
  Member "re ranking person re identification with k reciprocal encoding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification")
  Member "gated siamese convolutional neural network architecture for human re identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "constrained dominant sets and its applications in computer vision")
  Member "person re identification for real world surveillance systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "learning a discriminative null space for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "few shot deep adversarial learning for video based person re identification")
  Member "deep ranking for person re identification via joint representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "few shot deep adversarial learning for video based person re identification")
  Member "scalable metric learning via weighted approximate rank component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improving person re identification by attribute and identity learning")
  Member "a siamese long short term memory architecture for human re identification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "person recognition in personal photo collections")
  Member "an enhanced deep feature representation for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning shape representations for clothing variations in 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)