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: "accelerating eulerian fluid simulation with convolutional networks"
  Abstract: Efficient simulation of the Navier-Stokes equations for fluid flow is a long standing problem in applied mathematics, for which state-of-the-art methods require large compute resources. In this work, we propose a data-driven approach that leverages the approximation power of deep-learning with the precision of standard solvers to obtain fast and highly realistic simulations. Our method solves the incompressible Euler equations using the standard operator splitting method, in which a large sparse linear system with many free parameters must be solved. We use a Convolutional Network with a highly tailored architecture, trained using a novel unsupervised learning framework to solve the linear system. We present real-time 2D and 3D simulations that outperform recently proposed data-driven methods; the obtained results are realistic and show good generalization properties.

Connected through hyperedge HE-1 (induced by source paper "fea net a physics guided data driven model for efficient mechanical response prediction"):
  with members:
    - "generalizing from a few examples a survey on few shot learning"
    - "meshcnn a network with an edge"
    - "learning neural pde solvers with convergence guarantees"
    - "a deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials"
    - "one shot generation of near optimal topology through theory driven machine learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural networks for topology optimization"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"

Connected through hyperedge HE-2 (induced by source paper "incorporating symmetry into deep dynamics models for improved generalization"):
  with members:
    - "deep scale spaces equivariance over scale"
    - "general e 2 equivariant steerable cnns"
    - "invariant representations through adversarial forgetting"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "equivariant neural networks and equivarification"
    - "invariant representations without adversarial training"
    - "neural ordinary differential equations"
    - "learning steerable filters for rotation equivariant cnns"

Connected through hyperedge HE-3 (induced by source paper "ada lista learned solvers adaptive to varying models"):
  with members:
    - "deep network classification by scattering and homotopy dictionary learning"
    - "deep energy using energy functions for unsupervised training of dnns"
    - "deep energy unsupervised training of deep neural networks"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "ista net interpretable optimization inspired deep network for image compressive sensing"
    - "learning to learn by gradient descent by gradient descent"
    - "the little engine that could regularization by denoising red"

Connected through hyperedge HE-4 (induced by source paper "using deep learning to extend the range of air pollution monitoring and forecasting"):
  with members:
    - "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"
    - "fd net with auxiliary time steps fast prediction of pdes using hessian free trust region methods"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "neural ordinary differential equations"
    - "high order graph convolutional recurrent neural network a deep learning framework for network scale traffic learning and forecasting"
    - "deep air learning interpolation prediction and feature analysis of fine grained air quality"
    - "machine learning of linear differential equations using gaussian processes"

Connected through hyperedge HE-5 (induced by source paper "comparing recurrent and convolutional neural networks for predicting wave propagation"):
  with members:
    - "deep fluids a generative network for parameterized fluid simulations"
    - "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"
    - "flownet learning optical flow with convolutional networks"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

Connected through hyperedge HE-6 (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"
    - "liquid splash modeling with neural networks"
    - "interpolations of smoke and liquid simulations"
    - "data driven synthesis of smoke flows with cnn based feature descriptors"
    - "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-7 (induced by source paper "learnet dynamic imaging network for micro expression recognition"):
  with members:
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "striving for simplicity the all convolutional net"

Connected through hyperedge HE-8 (induced by source paper "irregular convolutional auto encoder on point clouds"):
  with members:
    - "deep fluids a generative network for parameterized fluid simulations"
    - "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"

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.CV(Computer Vision and Pattern Recognition)