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: "tempogan a temporally coherent volumetric gan for super resolution fluid flow"
  Abstract: We propose a temporally coherent generative model addressing the super-resolution problem for fluid flows. Our work represents a first approach to synthesize four-dimensional physics fields with neural networks. Based on a conditional generative adversarial network that is designed for the inference of three-dimensional volumetric data, our model generates consistent and detailed results by using a novel temporal discriminator, in addition to the commonly used spatial one. Our experiments show that the generator is able to infer more realistic high-resolution details by using additional physical quantities, such as low-resolution velocities or vorticities. Besides improvements in the training process and in the generated outputs, these inputs offer means for artistic control as well. We additionally employ a physics-aware data augmentation step, which is crucial to avoid overfitting and to reduce memory requirements. In this way, our network learns to generate advected quantities with highly detailed, realistic, and temporally coherent features. Our method works instantaneously, using only a single time-step of low-resolution fluid data. We demonstrate the abilities of our method using a variety of complex inputs and applications in two and three dimensions.

Connected through hyperedge HE-1 (induced by source paper "latent space subdivision stable and controllable time predictions for fluid flow"):
  with members:
    - "deep fluids a generative network for parameterized fluid simulations"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "liquid splash modeling with neural networks"
    - "interpolations of smoke and liquid simulations"
    - "data driven synthesis of smoke flows with cnn based feature descriptors"
    - "accelerating eulerian fluid simulation with convolutional networks"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "transport based neural style transfer for smoke simulations"):
  with members:
    - "deep fluids a generative network for parameterized fluid simulations"
    - "coupled fluid density and motion from single views"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "neural style transfer a review"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "data driven synthesis of smoke flows with cnn based feature descriptors"
    - "neural 3d mesh renderer"

Connected through hyperedge HE-3 (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"
    - "deep scattering rendering atmospheric clouds with radiance predicting neural networks"
    - "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-4 (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"
    - "deep scattering rendering atmospheric clouds with radiance predicting neural networks"
    - "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-5 (induced by source paper "realtime simulation of thin shell deformable materials using cnn based mesh embedding"):
  with members:
    - "fast motion planning for high dof robot systems using hierarchical system identification"
    - "hierarchical cloth simulation using deep neural networks"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "cloth manipulation using random forest based controller parametrization"
    - "sparse data driven mesh deformation"
    - "variational autoencoders for deforming 3d mesh models"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "mesh based autoencoders for localized deformation component analysis"

Connected through hyperedge HE-6 (induced by source paper "dynamic upsampling of smoke through dictionary based learning"):
  with members:
    - "deep fluids a generative network for parameterized fluid simulations"
    - "a multi pass gan for fluid flow super resolution"
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "continuous scale kinetic fluid simulation"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "liquid splash modeling with neural networks"
    - "data driven synthesis of smoke flows with cnn based feature descriptors"

Connected through hyperedge HE-7 (induced by source paper "efficient cloth simulation using miniature cloth and upscaling deep neural networks"):
  with members:
    - "nnwarp neural network based nonlinear deformation"
    - "anderson acceleration for geometry optimization and physics simulation"
    - "hierarchical cloth simulation using deep neural networks"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "accelerating eulerian fluid simulation with convolutional networks"
    - "a compositional object based approach to learning physical dynamics"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "learning temporal coherence via self supervision for gan based video generation"):
  with members:
    - "mocycle gan unpaired video to video translation"
    - "learning correspondence from the cycle consistency of time"
    - "preserving semantic and temporal consistency for unpaired video to video translation"
    - "frame recurrent video super resolution"
    - "video to video synthesis"
    - "recycle gan unsupervised video retargeting"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "the perception distortion tradeoff"

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)