human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "shape reconstruction by learning differentiable surface representations"):
  Abstract: Generative models that produce point clouds have emerged as a powerful tool to represent 3D surfaces, and the best current ones rely on learning an ensemble of parametric representations. Unfortunately, they offer no control over the deformations of the surface patches that form the ensemble and thus fail to prevent them from either overlapping or collapsing into single points or lines. As a consequence, computing shape properties such as surface normals and curvatures becomes difficult and unreliable. #R##N#In this paper, we show that we can exploit the inherent differentiability of deep networks to leverage differential surface properties during training so as to prevent patch collapse and strongly reduce patch overlap. Furthermore, this lets us reliably compute quantities such as surface normals and curvatures. We will demonstrate on several tasks that this yields more accurate surface reconstructions than the state-of-the-art methods in terms of normals estimation and amount of collapsed and overlapped patches.

Includes members:
  Member "learning elementary structures for 3d shape generation and matching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "total3dunderstanding joint layout object pose and mesh reconstruction for indoor scenes from a single image")
  Member "geometry aware network for non rigid shape prediction from a single view":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "3dpeople modeling the geometry of dressed humans")
  Member "deep learning parametrization for b spline curve approximation":
    also connected through other hyperedges:
      (none)
  Member "garnet a two stream network for fast and accurate 3d cloth draping":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "pixels voxels and views a study of shape representations for single view 3d object shape prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "conditional single view shape generation for multi view stereo reconstruction")
  Member "deep geometric prior for surface reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "implicit geometric regularization for learning shapes")
  Member "foldingnet point cloud auto encoder via deep grid deformation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "surfnet generating 3d shape surfaces using deep residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")

Question: Please tell me which class the source paper 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)