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 "interpretable and generalizable deep image matching with adaptive convolutions"):
  Abstract: For image matching tasks, like face recognition and person re-identification, existing deep networks often focus on representation learning. However, without domain adaptation or transfer learning, the learned model is fixed as is, which is not adaptable to handle various unseen scenarios. In this paper, beyond representation learning, we consider how to formulate image matching directly in deep feature maps. We treat image matching as finding local correspondences in feature maps, and construct adaptive convolution kernels on the fly to achieve local matching. In this way, the matching process and result is interpretable, and this explicit matching is more generalizable than representation features to unseen scenarios, such as unknown misalignments, pose or viewpoint changes. To facilitate end-to-end training of such an image matching architecture, we further build a class memory module to cache feature maps of the most recent samples of each class, so as to compute image matching losses for metric learning. The proposed method is preliminarily validated on the person re-identification task. Through direct cross-dataset evaluation without further transfer learning, it achieves better results than many transfer learning methods. Besides, a model-free temporal cooccurrence based score weighting method is proposed, which improves the performance to a further extent, resulting in state-of-the-art results in cross-dataset evaluation.

Includes members:
  Member "what and where to match deep spatially multiplicative integration networks for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "few shot deep adversarial learning for video based person re identification")
  Member "learning discriminative features with multiple granularities for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "building computationally efficient and well generalizing person re identification models with metric learning")
  Member "multi task mid level feature alignment network for unsupervised cross dataset person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "memorizing comprehensively to learn adaptively unsupervised cross domain person re id with multi level memory")
  Member "arcface additive angular margin loss for deep face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "transferable joint attribute identity deep learning for unsupervised person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "dual attention matching network for context aware feature sequence based person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "unsupervised domain adaptive re identification theory and practice":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification")
  Member "adaptation and re identification network an unsupervised deep transfer learning approach to 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)