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 "multi feature distance metric learning for non rigid 3d shape retrieval"):
  Abstract: In the past decades, feature-learning-based 3D shape retrieval approaches have been received widespread attention in the computer graphic community. These approaches usually explored the hand-crafted distance metric or conventional distance metric learning methods to compute the similarity of the single feature. The single feature always contains onefold geometric information, which cannot characterize the 3D shapes well. Therefore, the multiple features should be used for the retrieval task to overcome the limitation of single feature and further improve the performance. However, most conventional distance metric learning methods fail to integrate the complementary information from multiple features to construct the distance metric. To address these issue, a novel multi-feature distance metric learning method for non-rigid 3D shape retrieval is presented in this study, which can make full use of the complimentary geometric information from multiple shape features by utilizing the KL-divergences. Minimizing KL-divergence between different metric of features and a common metric is a consistency constraints, which can lead the consistency shared latent feature space of the multiple features. We apply the proposed method to 3D model retrieval, and test our method on well known benchmark database. The results show that our method substantially outperforms the state-of-the-art non-rigid 3D shape retrieval methods.

Includes members:
  Member "3 d personvlad learning deep global representations for video based person reidentification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "few shot deep adversarial learning for video based person re identification")
  Member "cycle consistent deep generative hashing for cross modal retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "few shot deep adversarial learning for video based person re identification")
  Member "deep adaptive feature embedding with local sample distributions for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "few shot deep adversarial learning for video based person re identification")
  Member "what and where to match deep spatially multiplicative integration networks 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 "multiview spectral clustering via structured low rank matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "attribute guided feature learning network for vehicle re identification")
  Member "where and when to look deep siamese attention networks for video based person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pay attention to the activations a modular attention mechanism for fine grained image recognition")
  Member "effective multi query expansions collaborative deep networks for robust landmark retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discriminative feature and dictionary learning with part aware model for vehicle re identification")
  Member "beyond low rank representations orthogonal clustering basis reconstruction with optimized graph structure for multi view spectral clustering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an introduction to person re identification with generative adversarial networks")

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)