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 fluids a generative network for parameterized fluid simulations"
  Abstract: This paper presents a novel generative model to synthesize fluid simulations from a set of reduced parameters. A convolutional neural network is trained on a collection of discrete, parameterizable fluid simulation velocity fields. Due to the capability of deep learning architectures to learn representative features of the data, our generative model is able to accurately approximate the training data set, while providing plausible interpolated in-betweens. The proposed generative model is optimized for fluids by a novel loss function that guarantees divergence-free velocity fields at all times. In addition, we demonstrate that we can handle complex parameterizations in reduced spaces, and advance simulations in time by integrating in the latent space with a second network. Our method models a wide variety of fluid behaviors, thus enabling applications such as fast construction of simulations, interpolation of fluids with different parameters, time re-sampling, latent space simulations, and compression of fluid simulation data. Reconstructed velocity fields are generated up to 700x faster than traditional CPU solvers, while achieving compression rates of over 1300x.

Connected through hyperedge HE-1 (induced by source paper "comparing recurrent and convolutional neural networks for predicting wave propagation"):
  with members:
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "learning a physical long term predictor"
    - "deep learning for physical processes incorporating prior scientific knowledge"
    - "physics informed deep learning part ii data driven discovery of nonlinear partial differential equations"
    - "accelerating eulerian fluid simulation with convolutional networks"
    - "flownet learning optical flow with convolutional networks"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

Connected through hyperedge HE-2 (induced by source paper "latent space subdivision stable and controllable time predictions for fluid flow"):
  with members:
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "tempogan a temporally coherent volumetric gan for super resolution 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-3 (induced by source paper "irregular convolutional auto encoder on point clouds"):
  with members:
    - "cgans with projection discriminator"
    - "hierarchical graph representation learning with differentiable pooling"
    - "point convolutional neural networks by extension operators"
    - "splatnet sparse lattice networks for point cloud processing"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"
    - "local spectral graph convolution for point set feature learning"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "flexible neural representation for physics prediction"

Connected through hyperedge HE-4 (induced by source paper "transport based neural style transfer for smoke simulations"):
  with members:
    - "coupled fluid density and motion from single views"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "tempogan a temporally coherent volumetric gan for super resolution 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-5 (induced by source paper "a multi pass gan for fluid flow super resolution"):
  with members:
    - "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"
    - "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-6 (induced by source paper "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"):
  with members:
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "exponential convergence of the deep neural network approximation for analytic functions"
    - "extracting structured dynamical systems using sparse optimization with very few samples"
    - "stable architectures for deep neural networks"
    - "model reduction with memory and the machine learning of dynamical systems"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "neural ordinary differential equations"
    - "learning equations for extrapolation and control"

Connected through hyperedge HE-7 (induced by source paper "dynamic upsampling of smoke through dictionary based learning"):
  with members:
    - "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"
    - "liquid splash modeling with neural networks"
    - "data driven synthesis of smoke flows with cnn based feature descriptors"

Connected through hyperedge HE-8 (induced by source paper "frequency aware reconstruction of fluid simulations with generative networks"):
  with members:
    - "frequency principle fourier analysis sheds light on deep neural networks"
    - "training behavior of deep neural network in frequency domain"
    - "phasenet for video frame interpolation"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "began boundary equilibrium generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "perceptual losses for real time style transfer and super resolution"

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)