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: "dense motion estimation for smoke"
  Abstract: Motion estimation for highly dynamic phenomena such as smoke is an open challenge for Computer Vision. Traditional dense motion estimation algorithms have difficulties with non-rigid and large motions, both of which are frequently observed in smoke motion. We propose an algorithm for dense motion estimation of smoke. Our algorithm is robust, fast, and has better performance over different types of smoke compared to other dense motion estimation algorithms, including state of the art and neural network approaches. The key to our contribution is to use skeletal flow, without explicit point matching, to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this paper we describe our algorithm in greater detail, and provide experimental evidence to support our claims.

Connected through hyperedge HE-1 (induced by source paper "coupled fluid density and motion from single views"):
  with members:
    - "interpolations of smoke and liquid simulations"
    - "generating liquid simulations with deformation aware neural networks"
    - "primal dual optimization for fluids"

Connected through hyperedge HE-2 (induced by source paper "automatic structural scene digitalization"):
  with members:
    - "blur robust optical flow using motion channel"
    - "video interpolation using optical flow and laplacian smoothness"
    - "drift robust non rigid optical flow enhancement for long sequences"
    - "towards the design of effective freehand gestural interaction for interactive tv"

Connected through hyperedge HE-3 (induced by source paper "learn to model motion from blurry footages"):
  with members:
    - "learning to extract motion from videos in convolutional neural networks"
    - "towards the design of effective freehand gestural interaction for interactive tv"
    - "learning to deblur"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-4 (induced by source paper "video interpolation using optical flow and laplacian smoothness"):
  with members:
    - "nonrigid optical flow ground truth for real world scenes with time varying shading effects"
    - "blur robust optical flow using motion channel"
    - "drift robust non rigid optical flow enhancement for long sequences"
    - "towards the design of effective freehand gestural interaction for interactive tv"
    - "touch less interactive augmented reality game on vision based wearable device"

Connected through hyperedge HE-5 (induced by source paper "blur robust optical flow using motion channel"):
  with members:
    - "nonrigid optical flow ground truth for real world scenes with time varying shading effects"
    - "video interpolation using optical flow and laplacian smoothness"
    - "drift robust non rigid optical flow enhancement for long sequences"

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)