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 physics towards learning the temporal evolution of fluid flow"):
  Abstract: Our work explores methods for the data-driven inference of temporal evolutions of physical functions with deep learning techniques. More specifically, we target fluid flow problems, and we propose a novel LSTM-based approach to predict the changes of the pressure field over time. The central challenge in this context is the high dimensionality of Eulerian space-time data sets. Key for arriving at a feasible algorithm is a technique for dimensionality reduction based on convolutional neural networks, as well as a special architecture for temporal prediction. We demonstrate that dense 3D+time functions of physics system can be predicted with neural networks, and we arrive at a neural-network based simulation algorithm with significant practical speed-ups. We demonstrate the capabilities of our method with a series of complex liquid simulations, and with a set of single-phase buoyancy simulations. With a set of trained networks, our method is more than two orders of magnitudes faster than a traditional pressure solver. Additionally, we present and discuss a series of detailed evaluations for the different components of our algorithm.

Includes members:
  Member "deep dynamical modeling and control of unsteady fluid flows":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural network architectures for stochastic control using the nonlinear feynman kac lemma")
  Member "tempogan a temporally coherent volumetric gan for super resolution fluid flow":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "latent space subdivision stable and controllable time predictions for fluid flow")
  Member "learning a physical long term predictor":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "predicting the physical dynamics of unseen 3d objects")
  Member "visual interaction networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "predicting the physical dynamics of unseen 3d objects")
  Member "liquid splash modeling with neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "latent space subdivision stable and controllable time predictions for fluid flow")
  Member "deep learning the physics of transport phenomena":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "encoding invariances in deep generative models")
  Member "deep photo style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "parameter free style projection for arbitrary style transfer")
  Member "data driven synthesis of smoke flows with cnn based feature descriptors":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fea net a physics guided data driven model for efficient mechanical response prediction")

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.LG(Machine Learning)