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: "blur robust optical flow using motion channel"
  Abstract: It is hard to estimate optical flow given a realworld video sequence with camera shake and other motion blur. In this paper, we first investigate the blur parameterization for video footage using near linear motion elements. we then combine a commercial 3D pose sensor with an RGB camera, in order to film video footage of interest together with the camera motion. We illustrates that this additional camera motion/trajectory channel can be embedded into a hybrid framework by interleaving an iterative blind deconvolution and warping based optical flow scheme. Our method yields improved accuracy within three other state-of-the-art baselines given our proposed ground truth blurry sequences; and several other realworld sequences filmed by our imaging system.

Connected through hyperedge HE-1 (induced by source paper "automatic structural scene digitalization"):
  with members:
    - "video interpolation using optical flow and laplacian smoothness"
    - "drift robust non rigid optical flow enhancement for long sequences"
    - "dense motion estimation for smoke"
    - "towards the design of effective freehand gestural interaction for interactive tv"

Connected through hyperedge HE-2 (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"
    - "drift robust non rigid optical flow enhancement for long sequences"
    - "dense motion estimation for smoke"
    - "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-3 (induced by source paper "drift robust non rigid optical flow enhancement for long sequences"):
  with members:
    - "video interpolation using optical flow and laplacian smoothness"
    - "towards the design of effective freehand gestural interaction for interactive tv"

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)