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: "liquid splash modeling with neural networks"
  Abstract: This paper proposes a new data-driven approach to model detailed splashes for liquid simulations with neural networks. Our model learns to generate small-scale splash detail for the fluid-implicit-particle method using training data acquired from physically parametrized, high resolution simulations. We use neural networks to model the regression of splash formation using a classifier together with a velocity modifier. For the velocity modification, we employ a heteroscedastic model. We evaluate our method for different spatial scales, simulation setups, and solvers. Our simulation results demonstrate that our model significantly improves visual fidelity with a large amount of realistic droplet formation and yields splash detail much more efficiently than finer discretizations.

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"
    - "tempogan a temporally coherent volumetric gan for super resolution fluid flow"
    - "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 "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"
    - "deep scattering rendering atmospheric clouds with radiance predicting 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-3 (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"
    - "deep scattering rendering atmospheric clouds with radiance predicting 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-4 (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"
    - "tempogan a temporally coherent volumetric gan for super resolution fluid flow"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "data driven synthesis of smoke flows with cnn based feature descriptors"

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 scattering rendering atmospheric clouds with radiance predicting neural networks"
    - "deep learning for physical processes incorporating prior scientific knowledge"
    - "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 "latent space physics towards learning the temporal evolution of fluid flow"):
  with members:
    - "deep dynamical modeling and control of unsteady fluid flows"
    - "tempogan a temporally coherent volumetric gan for super resolution fluid flow"
    - "learning a physical long term predictor"
    - "visual interaction networks"
    - "deep learning the physics of transport phenomena"
    - "deep photo style transfer"
    - "data driven synthesis of smoke flows with cnn based feature descriptors"
    - "generating liquid simulations with deformation aware neural networks"

Connected through hyperedge HE-7 (induced by source paper "well how accurate is it a study of deep learning methods for reynolds averaged navier stokes simulations"):
  with members:
    - "hidden fluid mechanics a navier stokes informed deep learning framework for assimilating flow visualization data"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "generating liquid simulations with deformation aware neural networks"
    - "reconstruction of three dimensional porous media using generative adversarial neural networks"
    - "long term forecasting using higher order tensor rnns"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "adam a method for stochastic optimization"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-8 (induced by source paper "hierarchical cloth simulation using deep neural networks"):
  with members:
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "data driven synthesis of smoke flows with cnn based feature descriptors"
    - "accelerating eulerian fluid simulation with convolutional networks"
    - "a compositional object based approach to learning physical dynamics"
    - "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.GR(Graphics)