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: "dynamic label graph matching for unsupervised video re identification"
  Abstract: Label estimation is an important component in an unsupervised person re-identification (re-ID) system. This paper focuses on cross-camera label estimation, which can be subsequently used in feature learning to learn robust re-ID models. Specifically, we propose to construct a graph for samples in each camera, and then graph matching scheme is introduced for cross-camera labeling association. While labels directly output from existing graph matching methods may be noisy and inaccurate due to significant cross-camera variations, this paper proposes a dynamic graph matching (DGM) method. DGM iteratively updates the image graph and the label estimation process by learning a better feature space with intermediate estimated labels. DGM is advantageous in two aspects: 1) the accuracy of estimated labels is improved significantly with the iterations; 2) DGM is robust to noisy initial training data. Extensive experiments conducted on three benchmarks including the large-scale MARS dataset show that DGM yields competitive performance to fully supervised baselines, and outperforms competing unsupervised learning methods.

Connected through hyperedge HE-1 (induced by source paper "intra camera supervised person re identification"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "joint discriminative and generative learning for person re identification"
    - "omni scale feature learning for person re identification"
    - "unsupervised tracklet person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "weakly supervised person re identification"
    - "harmonious attention network for person re identification"

Connected through hyperedge HE-2 (induced by source paper "progressive cross camera soft label learning for semi supervised person re identification"):
  with members:
    - "camera adversarial transfer for unsupervised person re identification"
    - "imitating targets from all sides an unsupervised transfer learning method for person re identification"
    - "unsupervised tracklet person re identification"
    - "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"
    - "label propagation for deep semi supervised learning"
    - "attribute aware attention model for fine grained representation learning"

Connected through hyperedge HE-3 (induced by source paper "adaptive exploration for unsupervised person re identification"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "auto reid searching for a part aware convnet for person re identification"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "active multi kernel domain adaptation for hyperspectral image classification"
    - "self supervised video hashing with hierarchical binary auto encoder"
    - "dissecting person re identification from the viewpoint of viewpoint"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"

Connected through hyperedge HE-4 (induced by source paper "adaptive graph representation learning for video person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interaction and aggregation network for person re identification"
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "learning context graph for person search"
    - "omni scale feature learning for person re identification"
    - "video person re identification by temporal residual learning"
    - "scan self and collaborative attention network for video person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"

Connected through hyperedge HE-5 (induced by source paper "gan based pose aware regulation for video based person re identification"):
  with members:
    - "multi pseudo regularized label for generated data in person re identification"
    - "deep cosine metric learning for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "deep metric learning by online soft mining and class aware attention"
    - "crossing generative adversarial networks for cross view person re identification"
    - "part aligned bilinear representations for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "camera style adaptation for person re identification"

Connected through hyperedge HE-6 (induced by source paper "adversarial camera alignment network for unsupervised cross camera person re identification"):
  with members:
    - "camera adversarial transfer for unsupervised person re identification"
    - "imitating targets from all sides an unsupervised transfer learning method for person re identification"
    - "unsupervised tracklet person re identification"
    - "a novel unsupervised camera aware domain adaptation framework for person re identification"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"
    - "re identification with consistent attentive siamese networks"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"

Connected through hyperedge HE-7 (induced by source paper "differentiation of blackbox combinatorial solvers"):
  with members:
    - "mipaal mixed integer program as a layer"
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "guiding high performance sat solvers with unsat core predictions"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "smart predict and optimize for hard combinatorial optimization problems"
    - "end to end learning and optimization on graphs"
    - "learning a sat solver from single bit supervision"

Connected through hyperedge HE-8 (induced by source paper "unsupervised tracklet person re identification"):
  with members:
    - "multi level factorisation net for person re identification"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"
    - "deep association learning for unsupervised video person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "domain adaptation through synthesis for unsupervised person re identification"
    - "unsupervised person re identification by deep learning tracklet association"
    - "image image domain adaptation with preserved self similarity and domain dissimilarity for person re identification"
    - "person re identification by deep joint learning of multi loss classification"

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)