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 scattering rendering atmospheric clouds with radiance predicting neural networks"
  Abstract: We present a technique for efficiently synthesizing images of atmospheric clouds using a combination of Monte Carlo integration and neural networks. The intricacies of Lorenz-Mie scattering and the high albedo of cloud-forming aerosols make rendering of clouds---e.g. the characteristic silverlining and the "whiteness" of the inner body---challenging for methods based solely on Monte Carlo integration or diffusion theory. We approach the problem differently. Instead of simulating all light transport during rendering, we pre-learn the spatial and directional distribution of radiant flux from tens of cloud exemplars. To render a new scene, we sample visible points of the cloud and, for each, extract a hierarchical 3D descriptor of the cloud geometry with respect to the shading location and the light source. The descriptor is input to a deep neural network that predicts the radiance function for each shading configuration. We make the key observation that progressively feeding the hierarchical descriptor into the network enhances the network's ability to learn faster and predict with higher accuracy while using fewer coefficients. We also employ a block design with residual connections to further improve performance. A GPU implementation of our method synthesizes images of clouds that are nearly indistinguishable from the reference solution within seconds to minutes. Our method thus represents a viable solution for applications such as cloud design and, thanks to its temporal stability, for high-quality production of animated content.

Connected through hyperedge HE-1 (induced by source paper "a multi pass gan for fluid flow super resolution"):
  with members:
    - "deep fluids a generative network for parameterized fluid simulations"
    - "learning temporal coherence via self supervision for gan based video generation"
    - "a fully progressive approach to single image super resolution"
    - "tempogan a temporally coherent volumetric gan for super resolution fluid flow"
    - "liquid splash modeling with neural networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "deep learning the physics of transport phenomena"
    - "enhanced deep residual networks for single image super resolution"

Connected through hyperedge HE-2 (induced by source paper "deep fluids a generative network for parameterized fluid simulations"):
  with members:
    - "image inpainting for irregular holes using partial convolutions"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "began boundary equilibrium generative adversarial networks"
    - "liquid splash modeling with neural networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "deep learning the physics of transport phenomena"
    - "interpolations of smoke and liquid simulations"
    - "reconstruction of three dimensional porous media using generative adversarial neural networks"

Connected through hyperedge HE-3 (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 shading convolutional neural networks for screen space shading"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "one shot radiance global illumination using convolutional autoencoders"):
  with members:
    - "deep shading convolutional neural networks for screen space shading"
    - "deep illumination approximating dynamic global illumination with generative adversarial network"
    - "learning light transport the reinforced way"
    - "integral equations and machine learning"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "tempogan a temporally coherent volumetric gan for super resolution fluid flow"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "deep learning for physical processes incorporating prior scientific knowledge"
    - "liquid splash modeling with neural networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "deep learning the physics of transport phenomena"
    - "deep photo style transfer"
    - "enhanced deep residual networks for single image super resolution"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-6 (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 shading convolutional neural networks for screen space shading"
    - "learning a multi view stereo machine"
    - "learning light transport the reinforced way"
    - "pointnet deep learning on point sets for 3d classification and segmentation"

Connected through hyperedge HE-7 (induced by source paper "gaussian material synthesis"):
  with members:
    - "deep shading convolutional neural networks for screen space shading"
    - "an intuitive control space for material appearance"
    - "photorealistic facial texture inference using deep neural networks"
    - "learning deconvolution network for semantic segmentation"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

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)