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: "drift robust non rigid optical flow enhancement for long sequences"
  Abstract: It is hard to densely track a nonrigid object in long term, which is a fundamental research issue in the computer vision community. This task often relies on estimating pairwise correspondences between images over time where the error is accumulated and leads to a drift issue. In this paper, we introduce a novel optimization framework with an Anchor Patch constraint. It is supposed to significantly reduce overall errors given long sequences containing non-rigidly deformable objects. Our framework can be applied to any dense tracking algorithm, e.g. optical flow. We demonstrate the success of our approach by showing significant error reduction on 6 popular optical flow algorithms applied to a range of real-world nonrigid benchmarks. We also provide quantitative analysis of our approach given synthetic occlusions and image noise.

Connected through hyperedge HE-1 (induced by source paper "ismo gan adversarial learning for monocular non rigid 3d reconstruction"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "geometry aware network for non rigid shape prediction from a single view"
    - "generative image inpainting with contextual attention"
    - "octnetfusion learning depth fusion from data"
    - "progressive growing of gans for improved quality stability and variation"
    - "feastnet feature steered graph convolutions for 3d shape analysis"
    - "better together joint reasoning for non rigid 3d reconstruction with specularities and shading"

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"
    - "dense motion estimation for smoke"
    - "towards the design of effective freehand gestural interaction for interactive tv"

Connected through hyperedge HE-3 (induced by source paper "nonrigid optical flow ground truth for real world scenes with time varying shading effects"):
  with members:
    - "video interpolation using optical flow and laplacian smoothness"
    - "towards the design of effective freehand gestural interaction for interactive tv"

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"
    - "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-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"
    - "dense motion estimation for smoke"

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)