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 "single image svbrdf capture with a rendering aware deep network"):
  Abstract: A deep learning method to capture spatially varying materials from a single picture. Our deep network uses procedural materials as training data, and image re-renderings as a measure of quality.

Includes members:
  Member "self normalizing neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "soft root sign activation function")
  Member "non local neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "progressive growing of gans for improved quality stability and variation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "modeling surface appearance from a single photograph using self augmented convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deltra deep light transport for projector camera systems")
  Member "real time user guided image colorization with learned deep priors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "chromagan adversarial picture colorization with semantic class distribution")
  Member "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "material editing using a physically based rendering network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a method for estimating reflectance map and material using deep learning with synthetic dataset")
  Member "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep domain adversarial image generation for domain generalisation")

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)