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 "multichannel kernel canonical correlation analysis for cross view person reidentification"):
  Abstract: In this article, we introduce a method to overcome one of the main challenges of person reidentification in multicamera networks, namely cross-view appearance changes. The proposed solution addresses the extreme variability of person appearance in different camera views by exploiting multiple feature representations. For each feature, kernel canonical correlation analysis with different kernels is employed to learn several projection spaces in which the appearance correlation between samples of the same person observed from different cameras is maximized. An iterative logistic regression is finally used to select and weight the contributions of each projection and perform the matching between the two views. Experimental evaluation shows that the proposed solution obtains comparable performance on the VIPeR and PRID 450s datasets and improves on the PRID and CUHK01 datasets with respect to the state of the art.

Includes members:
  Member "transferring a semantic representation for person re identification and search":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised person re identification by deep asymmetric metric embedding")
  Member "learning to rank in person re identification with metric ensembles":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised person re identification by deep asymmetric metric embedding")
  Member "large scale approximate kernel canonical correlation analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey of multi view representation learning")
  Member "person re identification with correspondence structure learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "few shot deep adversarial learning for video based person re identification")
  Member "person re identification by local maximal occurrence representation and metric learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "deep metric learning for practical person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "person recognition in personal photo collections")

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)