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 "photo geometric autoencoding to learn 3d objects from unlabelled images"):
  Abstract: We show that generative models can be used to capture visual geometry constraints statistically. We use this fact to infer the 3D shape of object categories from raw single-view images. Differently from prior work, we use no external supervision, nor do we use multiple views or videos of the objects. We achieve this by a simple reconstruction task, exploiting the symmetry of the objects' shape and albedo. Specifically, given a single image of the object seen from an arbitrary viewpoint, our model predicts a symmetric canonical view, the corresponding 3D shape and a viewpoint transformation, and trains with the goal of reconstructing the input view, resembling an auto-encoder. Our experiments show that this method can recover the 3D shape of human faces, cat faces, and cars from single view images, without supervision. On benchmarks, we demonstrate superior accuracy compared to other methods that use 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 "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "audio driven talking face video generation with learning based personalized head pose")
  Member "unsupervised 3d pose estimation with geometric self supervision":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "lifting autoencoders unsupervised learning of a fully disentangled 3d morphable model using deep non rigid structure from motion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised learning of probably symmetric deformable 3d objects from images in the wild")
  Member "an adversarial neuro tensorial approach for learning disentangled representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised learning of probably symmetric deformable 3d objects from images in the wild")
  Member "unsupervised 3d shape learning from image collections in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self supervised single view 3d reconstruction via semantic consistency")
  Member "learning view priors for single view 3d reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "self supervised single view 3d reconstruction via semantic consistency")
  Member "discovery of latent 3d keypoints via end to end geometric reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)