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: "person re identification by local maximal occurrence representation and metric learning"
  Abstract: Person re-identification is an important technique towards automatic search of a person's presence in a surveillance video. Two fundamental problems are critical for person re-identification, feature representation and metric learning. An effective feature representation should be robust to illumination and viewpoint changes, and a discriminant metric should be learned to match various person images. In this paper, we propose an effective feature representation called Local Maximal Occurrence (LOMO), and a subspace and metric learning method called Cross-view Quadratic Discriminant Analysis (XQDA). The LOMO feature analyzes the horizontal occurrence of local features, and maximizes the occurrence to make a stable representation against viewpoint changes. Besides, to handle illumination variations, we apply the Retinex transform and a scale invariant texture operator. To learn a discriminant metric, we propose to learn a discriminant low dimensional subspace by cross-view quadratic discriminant analysis, and simultaneously, a QDA metric is learned on the derived subspace. We also present a practical computation method for XQDA, as well as its regularization. Experiments on four challenging person re-identification databases, VIPeR, QMUL GRID, CUHK Campus, and CUHK03, show that the proposed method improves the state-of-the-art rank-1 identification rates by 2.2%, 4.88%, 28.91%, and 31.55% on the four databases, respectively.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "learning to adapt invariance in memory for person re identification"
    - "self training with progressive augmentation for unsupervised cross domain person re identification"
    - "cross dataset person re identification via unsupervised pose disentanglement and adaptation"
    - "a novel unsupervised camera aware domain adaptation framework for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "person re identification by camera correlation aware feature augmentation"

Connected through hyperedge HE-2 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "recover and identify a generative dual model for cross resolution person re identification"
    - "joint discriminative and generative learning for person re identification"
    - "cross dataset person re identification via unsupervised pose disentanglement and adaptation"
    - "improving person re identification by attribute and identity learning"
    - "cgans with projection discriminator"
    - "multi level factorisation net for person re identification"
    - "learning to adapt structured output space for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "learning shape representations for clothing variations in person re identification"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "recover and identify a generative dual model for cross resolution person re identification"
    - "joint discriminative and generative learning for person re identification"
    - "rgb infrared cross modality person re identification via joint pixel and feature alignment"
    - "cross dataset person re identification via unsupervised pose disentanglement and adaptation"
    - "deep representation learning with part loss for person re identification"
    - "learning resolution invariant deep representations for person re identification"
    - "improving person re identification by attribute and identity learning"

Connected through hyperedge HE-4 (induced by source paper "cross modality person re identification with shared specific feature transfer"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "hpiln a feature learning framework for cross modality person re identification"
    - "learning disentangled representation for robust person re identification"
    - "a new benchmark and approach for fine grained cross media retrieval"
    - "joint discriminative and generative learning for person re identification"
    - "beyond intra modality discrepancy a comprehensive survey of heterogeneous person re identification"
    - "mtfh a matrix tri factorization hashing framework for efficient cross modal retrieval"
    - "attend to the difference cross modality person re identification via contrastive correlation"

Connected through hyperedge HE-5 (induced by source paper "few shot deep adversarial learning for video based person re identification"):
  with members:
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "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"
    - "crossing generative adversarial networks for cross view person re identification"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "person re identification via recurrent feature aggregation"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"

Connected through hyperedge HE-6 (induced by source paper "unsupervised person re identification by deep asymmetric metric embedding"):
  with members:
    - "person re identification by camera correlation aware feature augmentation"
    - "began boundary equilibrium generative adversarial networks"
    - "cross view asymmetric metric learning for unsupervised person re identification"
    - "end to end comparative attention networks for person re identification"
    - "transferring a semantic representation for person re identification and search"
    - "unsupervised person re identification clustering and fine tuning"
    - "person transfer gan to bridge domain gap for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"

Connected through hyperedge HE-7 (induced by source paper "high order information matters learning relation and topology for occluded person re identification"):
  with members:
    - "learning combinatorial embedding networks for deep graph matching"
    - "deep high resolution representation learning for human pose estimation"
    - "a novel teacher student learning framework for occluded person re identification"
    - "stnreid deep convolutional networks with pairwise spatial transformer networks for partial person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "maskreid a mask based deep ranking neural network for person re identification"
    - "scpnet spatial channel parallelism network for joint holistic and partial person re identification"

Connected through hyperedge HE-8 (induced by source paper "cross spectrum dual subspace pairing for rgb infrared cross modality person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "person re identification with metric learning using privileged information"
    - "feature affinity based pseudo labeling for semi supervised person re identification"
    - "person re identification with deep similarity guided graph neural network"
    - "fd gan pose guided feature distilling gan for robust person re identification"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "in defense of the triplet loss for person re identification"
    - "deep mutual learning"

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)