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 "deep learning the latent space of light transport"):
  Abstract: We suggest a method to directly deep-learn light transport, i. e., the mapping from a 3D geometry-illumination-material configuration to a shaded 2D image. While many previous learning methods have employed 2D convolutional neural networks applied to images, we show for the first time that light transport can be learned directly in 3D. The benefit of 3D over 2D is, that the former can also correctly capture illumination effects related to occluded and/or semi-transparent geometry. To learn 3D light transport, we represent the 3D scene as an unstructured 3D point cloud, which is later, during rendering, projected to the 2D output image. Thus, we suggest a two-stage operator comprising of a 3D network that first transforms the point cloud into a latent representation, which is later on projected to the 2D output image using a dedicated 3D-2D network in a second step. We will show that our approach results in improved quality in terms of temporal coherence while retaining most of the computational efficiency of common 2D methods. As a consequence, the proposed two stage-operator serves as a valuable extension to modern deferred shading approaches.

Includes members:
  Member "monte carlo convolution for learning on non uniformly sampled point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pointasnl robust point clouds processing using nonlocal neural networks with adaptive sampling")
  Member "end to end sampling patterns":
    also connected through other hyperedges:
      (none)
  Member "learning to importance sample in primary sample space":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural importance sampling")
  Member "rendernet a deep convolutional network for differentiable rendering from 3d shapes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images")
  Member "deep scattering rendering atmospheric clouds with radiance predicting neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a multi pass gan for fluid flow super resolution")
  Member "deep shading convolutional neural networks for screen space shading":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deltra deep light transport for projector camera systems")
  Member "learning a multi view stereo machine":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "learning light transport the reinforced way":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "one shot radiance global illumination using convolutional autoencoders")

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.GR(Graphics)