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: "optical flow based real time moving object detection in unconstrained scenes"
  Abstract: Real-time moving object detection in unconstrained scenes is a difficult task due to dynamic background, changing foreground appearance and limited computational resource. In this paper, an optical flow based moving object detection framework is proposed to address this problem. We utilize homography matrixes to online construct a background model in the form of optical flow. When judging out moving foregrounds from scenes, a dual-mode judge mechanism is designed to heighten the system's adaptation to challenging situations. In experiment part, two evaluation metrics are redefined for more properly reflecting the performance of methods. We quantitatively and qualitatively validate the effectiveness and feasibility of our method with videos in various scene conditions. The experimental results show that our method adapts itself to different situations and outperforms the state-of-the-art methods, indicating the advantages of optical flow based methods.

Connected through hyperedge HE-1 (induced by source paper "high performance visual object tracking with unified convolutional networks"):
  with members:
    - "exploiting offset guided network for pose estimation and tracking"
    - "state aware re identification feature for multi target multi camera tracking"
    - "fastpose towards real time pose estimation and tracking via scale normalized multi task networks"
    - "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-2 (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"
    - "state aware re identification feature for multi target multi camera tracking"
    - "fastpose towards real time pose estimation and tracking via scale normalized multi task networks"
    - "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-3 (induced by source paper "motion control on bionic eyes a comprehensive review"):
  with members:
    - "optical flow based online moving foreground analysis"
    - "action machine rethinking action recognition in trimmed videos"
    - "distractor aware siamese networks for visual object tracking"
    - "attention guided unified network for panoptic segmentation"
    - "an efficient optical flow based motion detection method for non stationary scenes"
    - "multi hierarchical independent correlation filters for visual tracking"
    - "uct learning unified convolutional networks for real time visual tracking"
    - "end to end video level representation learning for action recognition"

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)