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 "unsupervised learning of probably symmetric deformable 3d objects from images in the wild"):
  Abstract: We propose a method to learn 3D deformable object categories from raw single-view images, without external supervision. The method is based on an autoencoder that factors each input image into depth, albedo, viewpoint and illumination. In order to disentangle these components without supervision, we use the fact that many object categories have, at least in principle, a symmetric structure. We show that reasoning about illumination allows us to exploit the underlying object symmetry even if the appearance is not symmetric due to shading. Furthermore, we model objects that are probably, but not certainly, symmetric by predicting a symmetry probability map, learned end-to-end with the other components of the model. Our experiments show that this method can recover very accurately the 3D shape of human faces, cat faces and cars from single-view images, without any supervision or a prior shape model. On benchmarks, we demonstrate superior accuracy compared to another method that uses supervision at the level of 2D image correspondences.

Includes members:
  Member "hologan unsupervised learning of 3d representations from natural images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images")
  Member "learning to predict 3d objects with an interpolation based differentiable renderer":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images")
  Member "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "audio driven talking face video generation with learning based personalized head pose")
  Member "learning single image 3d reconstruction by generative modelling of shape pose and shading":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inverse graphics gan learning to generate 3d shapes from unstructured 2d data")
  Member "unsupervised generative 3d shape learning from natural images":
    also connected through other hyperedges:
      (none)
  Member "c3dpo canonical 3d pose networks for non rigid structure from motion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "weakly supervised 3d human pose learning via multi view images in the wild")
  Member "learning to regress 3d face shape and expression from an image without 3d supervision":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards high fidelity 3d face reconstruction from in the wild images using graph convolutional networks")
  Member "unsupervised 3d pose estimation with geometric self supervision":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")

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)