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 image registration and deformable surface reconstruction in presence of occlusions and minimal texture"
  Abstract: Deformable surface tracking from monocular images is well-known to be under-constrained. Occlusions often make the task even more challenging, and can result in failure if the surface is not sufficiently textured. In this work, we explicitly address the problem of 3D reconstruction of poorly textured, occluded surfaces, proposing a framework based on a template-matching approach that scales dense robust features by a relevancy score. Our approach is extensively compared to current methods employing both local feature matching and dense template alignment. We test on standard datasets as well as on a new dataset (that will be made publicly available) of a sparsely textured, occluded surface. Our framework achieves state-of-the-art results for both well and poorly textured, occluded surfaces.

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 "shape reconstruction by learning differentiable surface representations"):
  with members:
    - "learning elementary structures for 3d shape generation and matching"
    - "geometry aware network for non rigid shape prediction from a single view"
    - "deep learning parametrization for b spline curve approximation"
    - "garnet a two stream network for fast and accurate 3d cloth draping"
    - "pixels voxels and views a study of shape representations for single view 3d object shape prediction"
    - "deep geometric prior for surface reconstruction"
    - "foldingnet point cloud auto encoder via deep grid deformation"
    - "surfnet generating 3d shape surfaces using deep residual networks"

Connected through hyperedge HE-3 (induced by source paper "super a surgical perception framework for endoscopic tissue manipulation with surgical robotics"):
  with members:
    - "surfelwarp efficient non volumetric single view dynamic reconstruction"
    - "open sourced reinforcement learning environments for surgical robotics"
    - "augmented reality predictive displays to help mitigate the effects of delayed telesurgery"
    - "fast and reliable autonomous surgical debridement with cable driven robots using a two phase calibration procedure"
    - "robust estimation of similarity transformation for visual object tracking"
    - "robot autonomy for surgery"
    - "volumedeform real time volumetric non rigid reconstruction"
    - "real time 3d tracking of articulated tools for robotic surgery"

Connected through hyperedge HE-4 (induced by source paper "3d reconstruction of deformable revolving object under heavy hand interaction"):
  with members:
    - "octnetfusion learning depth fusion from data"

Connected through hyperedge HE-5 (induced by source paper "accurate 3d reconstruction of dynamic scenes from monocular image sequences with severe occlusions"):
  with members:
    - "better together joint reasoning for non rigid 3d reconstruction with specularities and shading"
    - "on the closed form solution of the rotation matrix arising in computer vision problems"

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)