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 "rgbd gan unsupervised 3d representation learning from natural image datasets via rgbd image synthesis"):
  Abstract: Understanding three-dimensional (3D) geometries from two-dimensional (2D) images without any labeled information is promising for understanding the real world without incurring annotation cost. We herein propose a novel generative model, RGBD-GAN, which achieves unsupervised 3D representation learning from 2D images. The proposed method enables camera parameter conditional image generation and depth image generation without any 3D annotations such as camera poses or depth. We used an explicit 3D consistency loss for two RGBD images generated from different camera parameters in addition to the ordinal GAN objective. The loss is simple yet effective for any type of image generator such as the DCGAN and StyleGAN to be conditioned on camera parameters. We conducted experiments and demonstrated that the proposed method could learn 3D representations from 2D images with various generator architectures.

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 "escaping plato s cave 3d shape from adversarial rendering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images")
  Member "depth from videos in the wild unsupervised monocular depth learning from unknown cameras":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning depth via interaction")
  Member "deepvoxels learning persistent 3d feature embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images")
  Member "learning view priors for single view 3d reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "self supervised single view 3d reconstruction via semantic consistency")
  Member "spectral normalization for generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "pixel2mesh generating 3d mesh models from single rgb images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "large scale gan training for high fidelity natural image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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)