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: "video interpolation using optical flow and laplacian smoothness"
  Abstract: Non-rigid video interpolation is a common computer vision task. In this paper we present an optical flow approach which adopts a Laplacian Cotangent Mesh constraint to enhance the local smoothness. Similar to Li et al., our approach adopts a mesh to the image with a resolution up to one vertex per pixel and uses angle constraints to ensure sensible local deformations between image pairs. The Laplacian Mesh constraints are expressed wholly inside the optical flow optimization, and can be applied in a straightforward manner to a wide range of image tracking and registration problems. We evaluate our approach by testing on several benchmark datasets, including the Middlebury and Garg et al. datasets. In addition, we show application of our method for constructing 3D Morphable Facial Models from dynamic 3D data.

Connected through hyperedge HE-1 (induced by source paper "automatic structural scene digitalization"):
  with members:
    - "blur robust optical flow using motion channel"
    - "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 "nonrigid optical flow ground truth for real world scenes with time varying shading effects"):
  with members:
    - "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 "blur robust optical flow using motion channel"):
  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"

Connected through hyperedge HE-4 (induced by source paper "drift robust non rigid optical flow enhancement for long sequences"):
  with members:
    - "blur robust optical flow using motion channel"
    - "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)