human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "latent space subdivision stable and controllable time predictions for fluid flow"):
  Abstract: We propose an end-to-end trained neural networkarchitecture to robustly predict the complex dynamics of fluid flows with high temporal stability. We focus on single-phase smoke simulations in 2D and 3D based on the incompressible Navier-Stokes (NS) equations, which are relevant for a wide range of practical problems. To achieve stable predictions for long-term flow sequences, a convolutional neural network (CNN) is trained for spatial compression in combination with a temporal prediction network that consists of stacked Long Short-Term Memory (LSTM) layers. Our core contribution is a novel latent space subdivision (LSS) to separate the respective input quantities into individual parts of the encoded latent space domain. This allows to distinctively alter the encoded quantities without interfering with the remaining latent space values and hence maximizes external control. By selectively overwriting parts of the predicted latent space points, our proposed method is capable to robustly predict long-term sequences of complex physics problems. In addition, we highlight the benefits of a recurrent training on the latent space creation, which is performed by the spatial compression network.

Includes members:
  Member "deep fluids a generative network for parameterized fluid simulations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "comparing recurrent and convolutional neural networks for predicting wave propagation")
  Member "latent space physics towards learning the temporal evolution of fluid flow":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "temporally folded convolutional neural networks for sequence forecasting")
  Member "tempogan a temporally coherent volumetric gan for super resolution fluid flow":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transport based neural style transfer for smoke simulations")
  Member "liquid splash modeling with neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a multi pass gan for fluid flow super resolution")
  Member "interpolations of smoke and liquid simulations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural re simulation for generating bounces in single images")
  Member "data driven synthesis of smoke flows with cnn based feature descriptors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fea net a physics guided data driven model for efficient mechanical response prediction")
  Member "accelerating eulerian fluid simulation with convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fea net a physics guided data driven model for efficient mechanical response prediction")
  Member "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")

Question: Please tell me which class the source paper 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)