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 "deepvoxels learning persistent 3d feature embeddings"):
  Abstract: In this work, we address the lack of 3D understanding of generative neural networks by introducing a persistent 3D feature embedding for view synthesis. To this end, we propose DeepVoxels, a learned representation that encodes the view-dependent appearance of a 3D scene without having to explicitly model its geometry. At its core, our approach is based on a Cartesian 3D grid of persistent embedded features that learn to make use of the underlying 3D scene structure. Our approach combines insights from 3D geometric computer vision with recent advances in learning image-to-image mappings based on adversarial loss functions. DeepVoxels is supervised, without requiring a 3D reconstruction of the scene, using a 2D re-rendering loss and enforces perspective and multi-view geometry in a principled manner. We apply our persistent 3D scene representation to the problem of novel view synthesis demonstrating high-quality results for a variety of challenging scenes.

Includes members:
  Member "unsupervised geometry aware representation for 3d human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "view extrapolation of human body from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "monocular neural image based rendering with continuous view control")
  Member "an intriguing failing of convolutional neural networks and the coordconv solution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "layer structured 3d scene inference via view synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "peelnet textured 3d reconstruction of human body using single view rgb image")
  Member "learning free form deformations for 3d object reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "rendernet a deep convolutional network for differentiable rendering from 3d shapes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images")
  Member "everybody dance now":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "transformation grounded image generation network for novel 3d view synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images")

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)