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 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.

Includes members:
  Member "a neural algorithm of artistic style":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deformation aware unpaired image translation for pose estimation on laboratory animals")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "deep convolutional inverse graphics network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "direct intrinsics learning albedo shading decomposition by convolutional regression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lifting autoencoders unsupervised learning of a fully disentangled 3d morphable model using deep non rigid structure from motion")
  Member "loss functions for neural networks for image processing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning for single image super resolution a brief review")
  Member "hypercolumns for object segmentation and fine grained localization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convolutional networks with dense connectivity")
  Member "designing deep networks for surface normal estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep iterative surface normal estimation")
  Member "caffe convolutional architecture for fast feature embedding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")

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)