human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "nonlinear local metric learning for person re identification"
  Abstract: Person re-identification aims at matching pedestrians observed from non-overlapping camera views. Feature descriptor and metric learning are two significant problems in person re-identification. A discriminative metric learning method should be capable of exploiting complex nonlinear transformations due to the large variations in feature space. In this paper, we propose a nonlinear local metric learning (NLML) method to improve the state-of-the-art performance of person re-identification on public datasets. Motivated by the fact that local metric learning has been introduced to handle the data which varies locally and deep neural network has presented outstanding capability in exploiting the nonlinearity of samples, we utilize the merits of both local metric learning and deep neural network to learn multiple sets of nonlinear transformations. By enforcing a margin between the distances of positive pedestrian image pairs and distances of negative pairs in the transformed feature subspace, discriminative information can be effectively exploited in the developed neural networks. Our experiments show that the proposed NLML method achieves the state-of-the-art results on the widely used VIPeR, GRID, and CUHK 01 datasets.

Connected through hyperedge HE-1 (induced by source paper "crossing generative adversarial networks for cross view person re identification"):
  with members:
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "scalable person re identification on supervised smoothed manifold"
    - "few shot adversarial domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "unsupervised image to image translation networks"
    - "cross view asymmetric metric learning for unsupervised person re identification"
    - "pose driven deep convolutional model for person re identification"

Connected through hyperedge HE-2 (induced by source paper "deep adaptive feature embedding with local sample distributions for person re identification"):
  with members:
    - "scalable person re identification on supervised smoothed manifold"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "re ranking person re identification with k reciprocal encoding"
    - "structured deep hashing with convolutional neural networks for fast person re identification"
    - "hcp a flexible cnn framework for multi label image classification"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "embedding deep metric for person re identication a study against large variations"
    - "learning a discriminative null space for person re identification"

Connected through hyperedge HE-3 (induced by source paper "scalable person re identification on supervised smoothed manifold"):
  with members:
    - "transferring a semantic representation for person re identification and search"
    - "learning a discriminative null space for person re identification"
    - "deep attributes driven multi camera person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "scalable metric learning via weighted approximate rank component analysis"
    - "human in the loop person re identification"
    - "joint detection and identification feature learning for person search"
    - "learning to rank in person re identification with metric ensembles"

Connected through hyperedge HE-4 (induced by source paper "kernel cross view collaborative representation based classification for person re identification"):
  with members:
    - "re ranking person re identification with k reciprocal encoding"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "person re identification for real world surveillance systems"
    - "learning a discriminative null space for person re identification"
    - "deep ranking for person re identification via joint representation learning"
    - "scalable metric learning via weighted approximate rank component analysis"
    - "a siamese long short term memory architecture for human re identification"
    - "an enhanced deep feature representation for person re identification"

Question: Please tell me which class the center node 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)