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: "an equalised global graphical model based approach for multi camera object tracking"
  Abstract: Non-overlapping multi-camera visual object tracking typically consists of two steps: single camera object tracking and inter-camera object tracking. Most of tracking methods focus on single camera object tracking, which happens in the same scene, while for real surveillance scenes, inter-camera object tracking is needed and single camera tracking methods can not work effectively. In this paper, we try to improve the overall multi-camera object tracking performance by a global graph model with an improved similarity metric. Our method treats the similarities of single camera tracking and inter-camera tracking differently and obtains the optimization in a global graph model. The results show that our method can work better even in the condition of poor single camera object tracking.

Connected through hyperedge HE-1 (induced by source paper "multi drone based single object tracking with agent sharing network"):
  with members:
    - "joint group feature selection and discriminative filter learning for robust visual object tracking"
    - "vision meets drones a challenge"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "features for multi target multi camera tracking and re identification"
    - "distractor aware siamese networks for visual object tracking"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "the unmanned aerial vehicle benchmark object detection and tracking"
    - "siamrpn evolution of siamese visual tracking with very deep networks"

Connected through hyperedge HE-2 (induced by source paper "cityflow a city scale benchmark for multi target multi camera vehicle tracking and re identification"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "moana an online learned adaptive appearance model for robust multiple object tracking in 3d"
    - "multi level factorisation net for person re identification"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "harmonious attention network for person re identification"
    - "features for multi target multi camera tracking and re identification"
    - "yolov3 an incremental improvement"
    - "the devil is in the middle exploiting mid level representations for cross domain instance matching"

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"
    - "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"
    - "person re identification by local maximal occurrence representation and metric learning"

Connected through hyperedge HE-4 (induced by source paper "unified framework for automated person re identification and camera network topology inference in camera networks"):
  with members:
    - "person re identification in the wild"
    - "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)