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: "state aware re identification feature for multi target multi camera tracking"
  Abstract: Multi-target Multi-camera Tracking (MTMCT) aims to extract the trajectories from videos captured by a set of cameras. Recently, the tracking performance of MTMCT is significantly enhanced with the employment of re-identification (Re-ID) model. However, the appearance feature usually becomes unreliable due to the occlusion and orientation variance of the targets. Directly applying Re-ID model in MTMCT will encounter the problem of identity switches (IDS) and tracklet fragment caused by occlusion. To solve these problems, we propose a novel tracking framework in this paper. In this framework, the occlusion status and orientation information are utilized in Re-ID model with human pose information considered. In addition, the tracklet association using the proposed fused tracking feature is adopted to handle the fragment problem. The proposed tracker achieves 81.3\% IDF1 on the multiple-camera hard sequence, which outperforms all other reference methods by a large margin.

Connected through hyperedge HE-1 (induced by source paper "building computationally efficient and well generalizing person re identification models with metric learning"):
  with members:
    - "deep learning for person re identification a survey and outlook"
    - "rethinking person re identification with confidence"
    - "res2net a new multi scale backbone architecture"
    - "omni scale feature learning for person re identification"
    - "attention a big surprise for cross domain person re identification"
    - "searching for a robust neural architecture in four gpu hours"
    - "spherereid deep hypersphere manifold embedding for person re identification"
    - "unsupervised person re identification by soft multilabel learning"

Connected through hyperedge HE-2 (induced by source paper "high performance visual object tracking with unified convolutional networks"):
  with members:
    - "exploiting offset guided network for pose estimation and tracking"
    - "fastpose towards real time pose estimation and tracking via scale normalized multi task networks"
    - "optical flow based real time moving object detection in unconstrained scenes"
    - "optical flow based online moving foreground analysis"
    - "action machine rethinking action recognition in trimmed videos"
    - "distractor aware siamese networks for visual object tracking"
    - "an efficient optical flow based motion detection method for non stationary scenes"
    - "multi hierarchical independent correlation filters for visual tracking"

Connected through hyperedge HE-3 (induced by source paper "human following for wheeled robot with monocular pan tilt camera"):
  with members:
    - "detection and tracking of small objects in sparse 3d laser range data"
    - "exploiting offset guided network for pose estimation and tracking"
    - "fastpose towards real time pose estimation and tracking via scale normalized multi task networks"
    - "optical flow based real time moving object detection in unconstrained scenes"
    - "action machine rethinking action recognition in trimmed videos"
    - "attention guided unified network for panoptic segmentation"
    - "an efficient optical flow based motion detection method for non stationary scenes"
    - "end to end active object tracking and its real world deployment via reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "multi stage hrnet multiple stage high resolution network for human pose estimation"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "human following for wheeled robot with monocular pan tilt camera"
    - "deep high resolution representation learning for human pose estimation"
    - "hybrid task cascade for instance segmentation"
    - "exploiting offset guided network for pose estimation and tracking"
    - "fastpose towards real time pose estimation and tracking via scale normalized multi task networks"
    - "action machine rethinking action recognition in trimmed videos"
    - "distractor aware siamese networks for visual object tracking"

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)