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: "joint person re identification and camera network topology inference in multiple cameras"
  Abstract: Person re-identification is the task of recognizing or identifying a person across multiple views in multi-camera networks. Although there has been much progress in person re-identification, person re-identification in large-scale multi-camera networks still remains a challenging task because of the large spatio-temporal uncertainty and high complexity due to a large number of cameras and people. To handle these difficulties, additional information such as camera network topology should be provided, which is also difficult to automatically estimate, unfortunately. In this study, we propose a unified framework which jointly solves both person re-identification and camera network topology inference problems with minimal prior knowledge about the environments. The proposed framework takes general multi-camera network environments into account and can be applied to online person re-identification in large-scale multi-camera networks. In addition, to effectively show the superiority of the proposed framework, we provide a new person re-identification dataset with full annotations, named SLP, captured in the multi-camera network consisting of nine non-overlapping cameras. Experimental results using our person re-identification and public datasets show that the proposed methods are promising for both person re-identification and camera topology inference tasks.

Connected through hyperedge HE-1 (induced by source paper "a strong baseline for fashion retrieval with person re identification models"):
  with members:
    - "fashion retrieval via graph reasoning networks on a similarity pyramid"
    - "mildnet a lightweight single scaled deep ranking architecture"
    - "auto reid searching for a part aware convnet for person re identification"
    - "learning generalisable omni scale representations for person re identification"
    - "a strong baseline and batch normalization neck for deep person re identification"
    - "spatial temporal person re identification"
    - "two at once enhancing learning and generalization capacities via ibn net"
    - "learning embeddings for product visual search with triplet loss and online sampling"

Connected through hyperedge HE-2 (induced by source paper "progressive unsupervised person re identification by tracklet association with spatio temporal regularization"):
  with members:
    - "unsupervised person re identification by soft multilabel learning"
    - "pose flow efficient online pose tracking"
    - "harmonious attention network for person re identification"
    - "person search by multi scale matching"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "unsupervised person re identification by deep learning tracklet association"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-3 (induced by source paper "an end to end foreground aware network for person re identification"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "person re identification with metric learning using privileged information"
    - "perceive where to focus learning visibility aware part level features for partial person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning incremental triplet margin for person re identification"
    - "harmonious attention network for person re identification"
    - "attention aware compositional network for person re identification"

Connected through hyperedge HE-4 (induced by source paper "spatial temporal person re identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"
    - "m2m gan many to many generative adversarial transfer learning for person re identification"
    - "occluded person re identification"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "cross domain visual matching via generalized similarity measure and feature learning"
    - "learning deep context aware features over body and latent parts 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)