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: "learning light transport the reinforced way"
  Abstract: We show that the equations of reinforcement learning and light transport simulation are related integral equations. Based on this correspondence, a scheme to learn importance while sampling path space is derived. The new approach is demonstrated in a consistent light transport simulation algorithm that uses reinforcement learning to progressively learn where light comes from. As using this information for importance sampling includes information about visibility, too, the number of light transport paths with zero contribution is dramatically reduced, resulting in much less noisy images within a fixed time budget.

Connected through hyperedge HE-1 (induced by source paper "one shot radiance global illumination using convolutional autoencoders"):
  with members:
    - "deep scattering rendering atmospheric clouds with radiance predicting neural networks"
    - "deep shading convolutional neural networks for screen space shading"
    - "deep illumination approximating dynamic global illumination with generative adversarial network"
    - "integral equations and machine learning"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "neural importance sampling"):
  with members:
    - "neural autoregressive flows"
    - "neural ordinary differential equations"
    - "learning to importance sample in primary sample space"
    - "progressive growing of gans for improved quality stability and variation"
    - "notes on optimal approximations for importance sampling"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "integral equations and machine learning"
    - "learning to learn by gradient descent by gradient descent"

Connected through hyperedge HE-3 (induced by source paper "deep learning the latent space of light transport"):
  with members:
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "end to end sampling patterns"
    - "learning to importance sample in primary sample space"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"
    - "deep scattering rendering atmospheric clouds with radiance predicting neural networks"
    - "deep shading convolutional neural networks for screen space shading"
    - "learning a multi view stereo machine"
    - "pointnet deep learning on point sets for 3d classification and segmentation"

Connected through hyperedge HE-4 (induced by source paper "light transport simulation via generalized multiple importance sampling"):
  with members:
    - "deep shading convolutional neural networks for screen space shading"

Connected through hyperedge HE-5 (induced by source paper "integral equations and machine learning"):
  with members:
    - "noise2noise learning image restoration without clean data"
    - "learning to importance sample in primary sample space"
    - "rainbow combining improvements in deep reinforcement learning"
    - "density estimation using real nvp"
    - "nice non linear independent components estimation"

Connected through hyperedge HE-6 (induced by source paper "notes on optimal approximations for importance sampling"):
  with members:
    - "charted metropolis light transport"

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.LG(Machine Learning)