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: "pamm pose aware multi shot matching for improving person re identification"
  Abstract: Person re-identification is the problem of recognizing people across different images or videos with non-overlapping views. Although a significant progress has been made in person re-identification over the last decade, it remains a challenging task because the appearances of people can seem extremely different across diverse camera viewpoints and person poses. In this paper, we propose a novel framework for person re-identification by analyzing camera viewpoints and person poses called pose-aware multi-shot matching. It robustly estimates individual poses and efficiently performs multi-shot matching based on the pose information. The experimental results obtained by using public person re-identification data sets show that the proposed methods outperform the current state-of-the-art methods, and are promising for accomplishing person re-identification under diverse viewpoints and pose variances.

Connected through hyperedge HE-1 (induced by source paper "improving description based person re identification by multi granularity image text alignments"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "stacked cross attention for image text matching"
    - "person search with natural language description"
    - "dual path convolutional image text embedding with instance loss"
    - "convolutional image captioning"
    - "vse improving visual semantic embeddings with hard negatives"
    - "learning semantic concepts and order for image and sentence matching"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"

Connected through hyperedge HE-2 (induced by source paper "towards better validity dispersion based clustering for unsupervised person re identification"):
  with members:
    - "person re identification with metric learning using privileged information"
    - "learning view specific deep networks for person re identification"
    - "feature affinity based pseudo labeling for semi supervised person re identification"
    - "deep clustering for unsupervised learning of visual features"
    - "deep association learning for unsupervised video person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "part aligned bilinear representations for person re identification"
    - "image image domain adaptation with preserved self similarity and domain dissimilarity for person re identification"

Connected through hyperedge HE-3 (induced by source paper "joint person re identification and camera network topology inference in multiple cameras"):
  with members:
    - "unified framework for automated person re identification and camera network topology inference in camera networks"
    - "person re identification in the wild"
    - "a comprehensive evaluation and benchmark for person re identification features metrics and datasets"
    - "performance measures and a data set for multi target multi camera tracking"
    - "an equalised global graphical model based approach for multi camera object tracking"
    - "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)