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: "generating liquid simulations with deformation aware neural networks"
  Abstract: We propose a novel approach for deformation-aware neural networks that learn the weighting and synthesis of dense volumetric deformation fields. Our method specifically targets the space-time representation of physical surfaces from liquid simulations. Liquids exhibit highly complex, non-linear behavior under changing simulation conditions such as different initial conditions. Our algorithm captures these complex phenomena in two stages: a first neural network computes a weighting function for a set of pre-computed deformations, while a second network directly generates a deformation field for refining the surface. Key for successful training runs in this setting is a suitable loss function that encodes the effect of the deformations, and a robust calculation of the corresponding gradients. To demonstrate the effectiveness of our approach, we showcase our method with several complex examples of flowing liquids with topology changes. Our representation makes it possible to rapidly generate the desired implicit surfaces. We have implemented a mobile application to demonstrate that real-time interactions with complex liquid effects are possible with our approach.

Connected through hyperedge HE-1 (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 learning the physics of transport phenomena"
    - "deep photo style transfer"
    - "enhanced deep residual networks for single image super resolution"

Connected through hyperedge HE-2 (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"
    - "liquid splash modeling with neural networks"
    - "deep learning the physics of transport phenomena"
    - "deep photo style transfer"
    - "data driven synthesis of smoke flows with cnn based feature descriptors"

Connected through hyperedge HE-3 (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"
    - "liquid splash modeling with neural networks"
    - "unpaired image to image translation using cycle consistent adversarial 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-4 (induced by source paper "coupled fluid density and motion from single views"):
  with members:
    - "interpolations of smoke and liquid simulations"
    - "primal dual optimization for fluids"
    - "dense motion estimation for smoke"

Connected through hyperedge HE-5 (induced by source paper "neuraldrop dnn based simulation of small scale liquid flows on solids"):
  with members:
    - "liquid splash modeling with neural networks"
    - "data driven synthesis of smoke flows with cnn based feature descriptors"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "liquid splash modeling with neural networks"):
  with members:
    - "tempogan a temporally coherent volumetric gan for super resolution fluid flow"
    - "data driven synthesis of smoke flows with cnn based feature descriptors"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "playing atari with deep reinforcement learning"

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)