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: "unsupervised adaptive re identification in open world dynamic camera networks"
  Abstract: Person re-identification is an open and challenging problem in computer vision. Existing approaches have concentrated on either designing the best feature representation or learning optimal matching metrics in a static setting where the number of cameras are fixed in a network. Most approaches have neglected the dynamic and open world nature of the re-identification problem, where a new camera may be temporarily inserted into an existing system to get additional information. To address such a novel and very practical problem, we propose an unsupervised adaptation scheme for re-identification models in a dynamic camera network. First, we formulate a domain perceptive re-identification method based on geodesic flow kernel that can effectively find the best source camera (already installed) to adapt with a newly introduced target camera, without requiring a very expensive training phase. Second, we introduce a transitive inference algorithm for re-identification that can exploit the information from best source camera to improve the accuracy across other camera pairs in a network of multiple cameras. Extensive experiments on four benchmark datasets demonstrate that the proposed approach significantly outperforms the state-of-the-art unsupervised learning based alternatives whilst being extremely efficient to compute.

Connected through hyperedge HE-1 (induced by source paper "intra camera supervised person re identification a new benchmark"):
  with members:
    - "unsupervised tracklet person re identification"
    - "multi level factorisation net for person re identification"
    - "harmonious attention network for person re identification"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"
    - "deep association learning for unsupervised video person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "unsupervised person re identification by deep learning tracklet association"
    - "deep mutual learning"

Connected through hyperedge HE-2 (induced by source paper "optimizing speed accuracy trade off for person re identification via knowledge distillation"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "learning incremental triplet margin for person re identification"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"
    - "re identification with consistent attentive siamese networks"
    - "maximum margin metric learning over discriminative nullspace for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "pyramidal person re identification via multi loss dynamic training"
    - "fd gan pose guided feature distilling gan for robust person re identification"

Connected through hyperedge HE-3 (induced by source paper "a cross modal distillation network for person re identification in rgb depth"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "features for multi target multi camera tracking and re identification"
    - "deep visual domain adaptation a survey"
    - "adaptation and re identification network an unsupervised deep transfer learning approach to person re identification"
    - "in defense of the triplet loss for person re identification"
    - "learning deep context aware features over body and latent parts for person re identification"
    - "a comprehensive survey on cross modal retrieval"
    - "person re identification past present and future"

Connected through hyperedge HE-4 (induced by source paper "one pass person re identification by sketch online discriminant analysis"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "person re identification by camera correlation aware feature augmentation"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "scalable person re identification on supervised smoothed manifold"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "adversarial attribute image person re identification"
    - "deep self paced learning for person re identification"
    - "svdnet for pedestrian retrieval"

Connected through hyperedge HE-5 (induced by source paper "dynamic label graph matching for unsupervised video re identification"):
  with members:
    - "unsupervised person re identification clustering and fine tuning"
    - "multi graph matching via affinity optimization with graduated consistency regularization"
    - "top push video based person re identification"
    - "sift meets cnn a decade survey of instance retrieval"
    - "person re identification by local maximal occurrence representation and metric 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)