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: "unified framework for automated person re identification and camera network topology inference in camera networks"
  Abstract: Person re-identification in large-scale multi-camera networks is a challenging task because of the spatio-temporal uncertainty and high complexity due to large numbers 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. In this paper, we propose a unified framework which jointly solves both person re-id and camera network topology inference problems. The proposed framework takes general multi-camera network environments into account. To effectively show the superiority of the proposed framework, we also provide a new person re-id dataset with full annotations, named SLP, captured in the synchronized multi-camera network. Experimental results show that the proposed methods are promising for both person re-id and camera topology inference tasks.

Connected through hyperedge HE-1 (induced by source paper "scaling video analytics systems to large camera deployments"):
  with members:
    - "person re identification by camera correlation aware feature augmentation"
    - "features for multi target multi camera tracking and re identification"
    - "nestdnn resource aware multi tenant on device deep learning for continuous mobile vision"
    - "focus querying large video datasets with low latency and low cost"
    - "noscope optimizing neural network queries over video at scale"
    - "person re identification in the wild"
    - "performance measures and a data set for multi target multi camera tracking"
    - "fast video classification via adaptive cascading of deep models"

Connected through hyperedge HE-2 (induced by source paper "distance based camera network topology inference for person re identification"):
  with members:
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "group re identification via unsupervised transfer of sparse features encoding"
    - "structured deep hashing with convolutional neural networks for fast person re identification"
    - "deep attributes driven multi camera person re identification"
    - "learning deep feature representations with domain guided dropout 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:
    - "pamm pose aware multi shot matching for improving person re identification"
    - "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)