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 "rendernet a deep convolutional network for differentiable rendering from 3d shapes"):
  Abstract: Traditional computer graphics rendering pipeline is designed for procedurally generating 2D quality images from 3D shapes with high performance. The non-differentiability due to discrete operations such as visibility computation makes it hard to explicitly correlate rendering parameters and the resulting image, posing a significant challenge for inverse rendering tasks. Recent work on differentiable rendering achieves differentiability either by designing surrogate gradients for non-differentiable operations or via an approximate but differentiable renderer. These methods, however, are still limited when it comes to handling occlusion, and restricted to particular rendering effects. We present RenderNet, a differentiable rendering convolutional network with a novel projection unit that can render 2D images from 3D shapes. Spatial occlusion and shading calculation are automatically encoded in the network. Our experiments show that RenderNet can successfully learn to implement different shaders, and can be used in inverse rendering tasks to estimate shape, pose, lighting and texture from a single image.

Includes members:
  Member "neural inverse rendering for general reflectance photometric stereo":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deltra deep light transport for projector camera systems")
  Member "unsupervised training for 3d morphable model regression":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "inverse graphics gan learning to generate 3d shapes from unstructured 2d data")
  Member "learning to generate and reconstruct 3d meshes with only 2d supervision":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self supervised single view 3d reconstruction via semantic consistency")
  Member "multi view consistency as supervisory signal for learning shape and pose prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "seqxy2seqz structure learning for 3d shapes by sequentially predicting 1d occupancy segments from 2d coordinates")
  Member "transformation grounded image generation network for novel 3d view synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images")
  Member "deep shading convolutional neural networks for screen space shading":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deltra deep light transport for projector camera systems")
  Member "octree generating networks efficient convolutional architectures for high resolution 3d outputs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "o cnn octree based convolutional neural networks for 3d shape analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on deep geometry learning from a representation perspective")

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)