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: "interpolations of smoke and liquid simulations"
  Abstract: We present a novel method to interpolate smoke and liquid simulations in order to perform data-driven fluid simulations. Our approach calculates a dense space-time deformation using grid-based signed-distance functions of the inputs.   A key advantage of this implicit Eulerian representation is that it allows us to use powerful techniques from the optical flow area. We employ a five-dimensional optical flow solve. In combination with a projection algorithm, and residual iterations, we achieve a robust matching of the inputs. Once the match is computed, arbitrary in-between variants can be created very efficiently. To concatenate multiple long-range deformations, we propose a novel alignment technique.   Our approach has numerous advantages, including automatic matches without user input, volumetric deformations that can be applied to details around the surface, and the inherent handling of topology changes. As a result, we can interpolate swirling smoke clouds, and splashing liquid simulations. We can even match and interpolate phenomena with fundamentally different physics: a drop of liquid, and a blob of heavy smoke.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "neural re simulation for generating bounces in single images"):
  with members:
    - "bounce and learn modeling scene dynamics with real world bounces"
    - "neural rerendering in the wild"
    - "lookingood enhancing performance capture with real time neural re rendering"
    - "ignor image guided neural object rendering"
    - "learning a physical long term predictor"
    - "visual interaction networks"
    - "learning to generate long term future via hierarchical prediction"
    - "learning to represent mechanics via long term extrapolation and interpolation"

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"
    - "deep learning the physics of transport phenomena"
    - "reconstruction of three dimensional porous media using generative adversarial neural networks"

Connected through hyperedge HE-4 (induced by source paper "coupled fluid density and motion from single views"):
  with members:
    - "generating liquid simulations with deformation aware neural networks"
    - "primal dual optimization for fluids"
    - "dense motion estimation for smoke"

Connected through hyperedge HE-5 (induced by source paper "generating liquid simulations with deformation aware neural networks"):
  with members:
    - "learning a physical long term predictor"
    - "deep learning the physics of transport phenomena"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "data driven synthesis of smoke flows with cnn based feature descriptors"
    - "reasoning about liquids via closed loop simulation"
    - "unsupervised learning for physical interaction through video prediction"
    - "optical flow estimation using a spatial pyramid network"
    - "to fall or not to fall a visual approach to physical stability prediction"

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)