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: "pedestrian re identification based on tree branch network with local and global learning"
  Abstract: Deep part-based methods in recent literature have revealed the great potential of learning local part-level representation for pedestrian image in the task of person re-identification. However, global features that capture discriminative holistic information of human body are usually ignored or not well exploited. This motivates us to investigate joint learning global and local features from pedestrian images. Specifically, in this work, we propose a novel framework termed tree branch network (TBN) for person re-identification. Given a pedestrain image, the feature maps generated by the backbone CNN, are partitioned recursively into several pieces, each of which is followed by a bottleneck structure that learns finer-grained features for each level in the hierarchical tree-like framework. In this way, representations are learned in a coarse-to-fine manner and finally assembled to produce more discriminative image descriptions. Experimental results demonstrate the effectiveness of the global and local feature learning method in the proposed TBN framework. We also show significant improvement in performance over state-of-the-art methods on three public benchmarks: Market-1501, CUHK-03 and DukeMTMC.

Connected through hyperedge HE-1 (induced by source paper "attkgcn attribute knowledge graph convolutional network for person re identification"):
  with members:
    - "learning context graph for person search"
    - "aanet attribute attention network for person re identifications"
    - "multi label image recognition with graph convolutional networks"
    - "attributes aided part detection and refinement for person re identification"
    - "hierarchical graph convolutional networks for semi supervised node classification"
    - "perceive where to focus learning visibility aware part level features for partial person re identification"
    - "improving person re identification by attribute and identity learning"
    - "attribute aware attention model for fine grained representation learning"

Connected through hyperedge HE-2 (induced by source paper "multi resolution overlapping stripes network for person re identification"):
  with members:
    - "spherereid deep hypersphere manifold embedding for person re identification"
    - "auto reid searching for a part aware convnet for person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "learning incremental triplet margin for person re identification"
    - "harmonious attention network for person re identification"
    - "resource aware person re identification across multiple resolutions"
    - "random erasing data augmentation"
    - "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"

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)