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: "data driven synthesis of smoke flows with cnn based feature descriptors"
  Abstract: We present a novel data-driven algorithm to synthesize high resolution flow simulations with reusable repositories of space-time flow data. In our work, we employ a descriptor learning approach to encode the similarity between fluid regions with differences in resolution and numerical viscosity. We use convolutional neural networks to generate the descriptors from fluid data such as smoke density and flow velocity. At the same time, we present a deformation limiting patch advection method which allows us to robustly track deformable fluid regions. With the help of this patch advection, we generate stable space-time data sets from detailed fluids for our repositories. We can then use our learned descriptors to quickly localize a suitable data set when running a new simulation. This makes our approach very efficient, and resolution independent. We will demonstrate with several examples that our method yields volumes with very high effective resolutions, and non-dissipative small scale details that naturally integrate into the motions of the underlying flow.

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 "nnwarp neural network based nonlinear deformation"):
  with members:
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deep learning in neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "learning deconvolution network for semantic segmentation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (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"
    - "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-4 (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"
    - "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"
    - "neural 3d mesh renderer"

Connected through hyperedge HE-5 (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"
    - "deep learning the physics of transport phenomena"

Connected through hyperedge HE-6 (induced by source paper "learning similarity metrics for numerical simulations"):
  with members:
    - "data efficient image recognition with contrastive predictive coding"
    - "learning representations by maximizing mutual information across views"
    - "nima neural image assessment"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "siamese networks for semantic pattern similarity"
    - "pieapp perceptual image error assessment through pairwise preference"
    - "learned perceptual image enhancement"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-7 (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"
    - "liquid splash modeling with neural networks"

Connected through hyperedge HE-8 (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"
    - "deep learning the physics of transport phenomena"
    - "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"

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)