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 "intra camera supervised person re identification"):
  Abstract: Existing person re-identification (re-id) methods mostly exploit a large set of cross-camera identity labelled training data. This requires a tedious data collection and annotation process, leading to poor scalability in practical re-id applications. On the other hand unsupervised re-id methods do not need identity label information, but they usually suffer from much inferior and insufficient model performance. To overcome these fundamental limitations, we propose a novel person re-identification paradigm based on an idea of independent per-camera identity annotation. This eliminates the most time-consuming and tedious inter-camera identity labelling process, significantly reducing the amount of human annotation efforts. Consequently, it gives rise to a more scalable and more feasible setting, which we call Intra-Camera Supervised (ICS) person re-id, for which we formulate a Multi-tAsk mulTi-labEl (MATE) deep learning method. Specifically, MATE is designed for self-discovering the cross-camera identity correspondence in a per-camera multi-task inference framework. Extensive experiments demonstrate the cost-effectiveness superiority of our method over the alternative approaches on three large person re-id datasets. For example, MATE yields 88.7% rank-1 score on Market-1501 in the proposed ICS person re-id setting, significantly outperforming unsupervised learning models and closely approaching conventional fully supervised learning competitors.

Includes members:
  Member "unsupervised person re identification by deep asymmetric metric embedding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "joint discriminative and generative learning 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 "omni scale feature learning for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "building computationally efficient and well generalizing person re identification models with metric learning")
  Member "unsupervised tracklet person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to adapt invariance in memory for person re identification")
  Member "unsupervised person re identification by soft multilabel 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 "invariance matters exemplar memory for domain adaptive person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "weakly supervised person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "progressive sample mining and representation learning for one shot person re identification with adversarial samples")
  Member "harmonious attention network for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")

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)