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 learning the physics of transport phenomena"
  Abstract: We have developed a new data-driven paradigm for the rapid inference, modeling and simulation of the physics of transport phenomena by deep learning. Using conditional generative adversarial networks (cGAN), we train models for the direct generation of solutions to steady state heat conduction and incompressible fluid flow purely on observation without knowledge of the underlying governing equations. Rather than using iterative numerical methods to approximate the solution of the constitutive equations, cGANs learn to directly generate the solutions to these phenomena, given arbitrary boundary conditions and domain, with high test accuracy (MAE$<$1\%) and state-of-the-art computational performance. The cGAN framework can be used to learn causal models directly from experimental observations where the underlying physical model is complex or unknown.

Connected through hyperedge HE-1 (induced by source paper "encoding invariances in deep generative models"):
  with members:
    - "enforcing constraints for interpolation and extrapolation in generative adversarial networks"
    - "spatially constrained generative adversarial networks for conditional image generation"
    - "constrained image generation using binarized neural networks with decision procedures"
    - "large scale gan training for high fidelity natural image synthesis"
    - "do gan loss functions really matter"
    - "on convergence and stability of gans"
    - "bias and generalization in deep generative models an empirical study"
    - "which training methods for gans do actually converge"

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"
    - "liquid splash modeling with neural networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "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"
    - "liquid splash modeling with neural networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "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 "deep learning for determining a near optimal topological design without any iteration"):
  with members:
    - "neural networks for topology optimization"
    - "progressive growing of gans for improved quality stability and variation"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "tacotron towards end to end speech synthesis"
    - "interactive 3d modeling with a generative adversarial network"
    - "machine learning augmented predictive modeling of turbulent separated flows over airfoils"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "accelerating eulerian fluid simulation with convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "deep generative design integration of topology optimization and generative models"):
  with members:
    - "deep learning for determining a near optimal topological design without any iteration"
    - "one shot generation of near optimal topology through theory driven machine learning"
    - "3d topology optimization using convolutional neural networks"
    - "neural networks for topology optimization"
    - "image quality assessment techniques show improved training and evaluation of autoencoder generative adversarial networks"
    - "causalgan learning causal implicit generative models with adversarial training"
    - "energy based generative adversarial network"
    - "accelerating eulerian fluid simulation with convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "learning neural pde solvers with convergence guarantees"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "the case for learned index structures"
    - "data driven synthesis of smoke flows with cnn based feature descriptors"
    - "learning to learn by gradient descent by gradient descent"
    - "machine learning augmented predictive modeling of turbulent separated flows over airfoils"
    - "accelerating eulerian fluid simulation with convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-7 (induced by source paper "fd net with auxiliary time steps fast prediction of pdes using hessian free trust region methods"):
  with members:
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "neural ordinary differential equations"
    - "physics informed deep learning part ii data driven discovery of nonlinear partial differential equations"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (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"
    - "liquid splash modeling with neural networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "deep photo style transfer"
    - "enhanced deep residual networks for single image super resolution"
    - "unpaired image to image translation using cycle consistent adversarial networks"

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)