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: "a diffusion and clustering based approach for finding coherent motions and understanding crowd scenes"
  Abstract: This paper addresses the problem of detecting coherent motions in crowd scenes and presents its two applications in crowd scene understanding: semantic region detection and recurrent activity mining. It processes input motion fields (e.g., optical flow fields) and produces a coherent motion field named thermal energy field. The thermal energy field is able to capture both motion correlation among particles and the motion trends of individual particles, which are helpful to discover coherency among them. We further introduce a two-step clustering process to construct stable semantic regions from the extracted time-varying coherent motions. These semantic regions can be used to recognize pre-defined activities in crowd scenes. Finally, we introduce a cluster-and-merge process, which automatically discovers recurrent activities in crowd scenes by clustering and merging the extracted coherent motions. Experiments on various videos demonstrate the effectiveness of our approach.

Connected through hyperedge HE-1 (induced by source paper "understanding crowd flow movements using active langevin model"):
  with members:
    - "estimation of linear motion in dense crowd videos using langevin model"
    - "crowd behavior analysis"
    - "crowd flow segmentation in compressed domain using crf"
    - "detection of salient regions in crowded scenes"
    - "faster and better a machine learning approach to corner detection"

Connected through hyperedge HE-2 (induced by source paper "simultaneous detection and removal of dynamic objects in multi view images"):
  with members:
    - "image inpainting for irregular holes using partial convolutions"
    - "generative image inpainting with contextual attention"
    - "deep image prior"
    - "full flow optical flow estimation by global optimization over regular grids"
    - "video inpainting of complex scenes"
    - "moving object detection by detecting contiguous outliers in the low rank representation"

Connected through hyperedge HE-3 (induced by source paper "estimation of linear motion in dense crowd videos using langevin model"):
  with members:
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "flownet learning optical flow with convolutional networks"
    - "crowd flow segmentation in compressed domain using crf"
    - "fully convolutional networks for semantic segmentation"

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)