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 "nasa neural articulated shape approximation"):
  Abstract: Efficient representation of articulated objects such as human bodies is an important problem in computer vision and graphics. To efficiently simulate deformation, existing approaches represent objects as meshes and deform them using skinning techniques. This paper introduces neural articulated shape approximation (NASA), a framework that enables efficient representation of articulated deformable objects using neural indicator functions parameterized by pose. In contrast to classic approaches, NASA avoids the need to convert between different representations. For occupancy testing, NASA circumvents the complexity of meshes and mitigates the issue of water-tightness. In comparison with regular grids and octrees, our approach provides high resolution without high memory use.

Includes members:
  Member "implicit functions in feature space for 3d shape reconstruction and completion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to transfer texture from clothing images to 3d humans")
  Member "amass archive of motion capture as surface shapes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deformation aware unpaired image translation for pose estimation on laboratory animals")
  Member "deferred neural rendering image synthesis using neural textures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "deepsdf learning continuous signed distance functions for shape representation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "cvxnets learnable convex decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "unsupervised part based disentangling of object shape and appearance":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "chained representation cycling learning to estimate 3d human pose and shape by cycling between representations")
  Member "what do single view 3d reconstruction networks learn":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "cerberus a multi headed derenderer":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative hierarchical models for parts objects and scenes")

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)