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 "cross view kernel similarity metric learning using pairwise constraints for person re identification"):
  Abstract: Person re-identification is the task of matching pedestrian images across non-overlapping cameras. In this paper, we propose a non-linear cross-view similarity metric learning for handling small size training data in practical re-ID systems. The method employs non-linear mappings combined with cross-view discriminative subspace learning and cross-view distance metric learning based on pairwise similarity constraints. It is a natural extension of XQDA from linear to non-linear mappings using kernels, and learns non-linear transformations for efficiently handling complex non-linearity of person appearance across camera views. Importantly, the proposed method is very computationally efficient. Extensive experiments on four challenging datasets shows that our method attains competitive performance against state-of-the-art methods.

Includes members:
  Member "multiple kernel fisher discriminant metric learning for person re identification":
    also connected through other hyperedges:
      (none)
  Member "person re identification with metric learning using privileged information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cross spectrum dual subspace pairing for rgb infrared cross modality person re identification")
  Member "a semi supervised maximum margin metric learning approach for small scale person re identification":
    also connected through other hyperedges:
      (none)
  Member "maximum margin metric learning over discriminative nullspace for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "knowledge distillation for end to end person search")
  Member "graph correspondence transfer for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pose guided multi granularity attention network for text based person search")
  Member "person re identification in identity regression space":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "progressive darts bridging the optimization gap for nas in the wild")
  Member "scalable person re identification on supervised smoothed manifold":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "constrained dominant sets and its applications in computer vision")
  Member "beyond triplet loss a deep quadruplet network for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "memorizing comprehensively to learn adaptively unsupervised cross domain person re id with multi level memory")

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)