human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "deep shading convolutional neural networks for screen space shading"
  Abstract: In computer vision, convolutional neural networks CNNs achieve unprecedented performance for inverse problems where RGB pixel appearance is mapped to attributes such as positions, normals or reflectance. In computer graphics, screen space shading has boosted the quality of real-time rendering, converting the same kind of attributes of a virtual scene back to appearance, enabling effects like ambient occlusion, indirect light, scattering and many more. In this paper we consider the diagonal problem: synthesizing appearance from given per-pixel attributes using a CNN. The resulting Deep Shading renders screen space effects at competitive quality and speed while not being programmed by human experts but learned from example images.

Connected through hyperedge HE-1 (induced by source paper "deltra deep light transport for projector camera systems"):
  with members:
    - "pro cam ssfm projector camera system for structure and spectral reflectance from motion"
    - "compennet end to end full projector compensation"
    - "kornia an open source differentiable computer vision library for pytorch"
    - "self calibrating deep photometric stereo networks"
    - "end to end projector photometric compensation"
    - "neural inverse rendering for general reflectance photometric stereo"
    - "a single shot per pose camera projector calibration system for imperfect planar targets"
    - "inverserendernet learning single image inverse rendering"

Connected through hyperedge HE-2 (induced by source paper "neural voxel renderer learning an accurate and controllable rendering tool"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "deferred neural rendering image synthesis using neural textures"
    - "revealing scenes by inverting structure from motion reconstructions"
    - "neural rendering and reenactment of human actor videos"
    - "transformable bottleneck networks"
    - "differentiable surface splatting for point based geometry processing"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"

Connected through hyperedge HE-3 (induced by source paper "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "geometry aware network for non rigid shape prediction from a single view"
    - "hairnet single view hair reconstruction using convolutional neural networks"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "unsupervised training for 3d morphable model regression"
    - "siclope silhouette based clothed people"
    - "single image svbrdf capture with a rendering aware deep network"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"

Connected through hyperedge HE-4 (induced by source paper "soft rasterizer a differentiable renderer for image based 3d reasoning"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "learning category specific mesh reconstruction from image collections"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "unsupervised training for 3d morphable model regression"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"
    - "single image svbrdf capture with a rendering aware deep network"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"
    - "nonlinear 3d face morphable model"

Connected through hyperedge HE-5 (induced by source paper "inferring 3d shapes from image collections using adversarial networks"):
  with members:
    - "multiresolution tree networks for 3d point cloud processing"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "3d shape reconstruction from sketches via multi view convolutional networks"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "neural 3d mesh renderer"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "unsupervised learning of 3d structure from images"

Connected through hyperedge HE-6 (induced by source paper "net2vis a visual grammar for automatically generating publication ready cnn architecture visualizations"):
  with members:
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "single image tomography 3d volumes from 2d cranial x rays"
    - "cnncomparator comparative analytics of convolutional neural networks"
    - "ssgan secure steganography based on generative adversarial networks"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-7 (induced by source paper "neural point based graphics"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "lookingood enhancing performance capture with real time neural re rendering"
    - "free form image inpainting with gated convolution"
    - "ignor image guided neural object rendering"
    - "deep surface light fields"
    - "progressive growing of gans for improved quality stability and variation"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-8 (induced by source paper "volumetric isosurface rendering with deep learning based super resolution"):
  with members:
    - "frame recurrent video super resolution"
    - "learning temporal coherence via self supervision for gan based video generation"
    - "tempogan a temporally coherent volumetric gan for super resolution fluid flow"
    - "deep scattering rendering atmospheric clouds with radiance predicting neural networks"
    - "detail revealing deep video super resolution"
    - "coherent online video style transfer"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"

Question: Please tell me which class the center node 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)