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 "towards unsupervised learning of generative models for 3d controllable image synthesis"):
  Abstract: In recent years, Generative Adversarial Networks have achieved impressive results in photorealistic image synthesis. This progress nurtures hopes that one day the classical rendering pipeline can be replaced by efficient models that are learned directly from images. However, current image synthesis models operate in the 2D domain where disentangling 3D properties such as camera viewpoint or object pose is challenging. Furthermore, they lack an interpretable and controllable representation. Our key hypothesis is that the image generation process should be modeled in 3D space as the physical world surrounding us is intrinsically three-dimensional. We define the new task of 3D controllable image synthesis and propose an approach for solving it by reasoning both in 3D space and in the 2D image domain. We demonstrate that our model is able to disentangle latent 3D factors of simple multi-object scenes in an unsupervised fashion from raw images. Compared to pure 2D baselines, it allows for synthesizing scenes that are consistent wrt. changes in viewpoint or object pose. We further evaluate various 3D representations in terms of their usefulness for this challenging task.

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 "genesis generative scene inference and sampling with object centric latent representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")
  Member "a layer based sequential framework for scene generation with gans":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "monet unsupervised scene decomposition and representation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")
  Member "singan learning a generative model from a single natural image":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta transfer learning for zero shot super resolution")
  Member "escaping plato s cave 3d shape from adversarial rendering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images")
  Member "layoutgan generating graphic layouts with wireframe discriminators":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "auto completion of user interface layout design using transformer based tree decoders")
  Member "structured3d a large photo realistic dataset for structured 3d modeling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scan2plan efficient floorplan generation from 3d scans of indoor 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)