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 "one shot radiance global illumination using convolutional autoencoders"):
  Abstract: Rendering realistic images with Global Illumination (GI) is a computationally demanding task and often requires dedicated hardware for feasible runtime. Recent projects have used Generative Adversarial Networks (GAN) to predict indirect lighting on an image level, but are limited to diffuse materials and require training on each scene. We present One-Shot Radiance (OSR), a novel machine learning technique for rendering Global Illumination using Convolutional Autoencoders. We combine a modern denoising Neural Network with Radiance Caching to offer high performance CPU GI rendering while supporting a wide range of material types, without the requirement of offline pre-computation or training for each scene. OSR has been evaluated on interior scenes, and is able to produce high-quality images within 180 seconds on a single CPU.

Includes members:
  Member "deep scattering rendering atmospheric clouds with radiance predicting neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "deltra deep light transport for projector camera systems")
  Member "deep illumination approximating dynamic global illumination with generative adversarial network":
    also connected through other hyperedges:
      (none)
  Member "learning light transport the reinforced way":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural importance sampling")
  Member "integral equations and machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural importance sampling")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure and robust machine learning for healthcare a survey")

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)