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 "dynamic upsampling of smoke through dictionary based learning"):
  Abstract: Simulating turbulent smoke flows is computationally intensive due to their intrinsic multiscale behavior, thus requiring relatively high resolution grids to fully capture their complexity. For iterative editing or simply faster generation of smoke flows, dynamic upsampling of an input low-resolution numerical simulation is an attractive, yet currently unattainable goal. In this paper, we propose a novel dictionary-based learning approach to the dynamic upsampling of smoke flows. For each frame of an input coarse animation, we seek a sparse representation of small, local velocity patches of the flow based on an over-complete dictionary, and use the resulting sparse coefficients to generate a high-resolution smoke animation sequence. We propose a novel dictionary-based neural network which learns both a fast evaluation of sparse patch encoding and a dictionary of corresponding coarse and fine patches from a sequence of example simulations computed with any numerical solver. Our upsampling network then injects into coarse input sequences physics-driven fine details, unlike most previous approaches that only employed fast procedural models to add high frequency to the input. We present a variety of upsampling results for smoke flows and offer comparisons to their corresponding high-resolution simulations to demonstrate the effectiveness of our approach.

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 "a multi pass gan for fluid flow super resolution":
    also connected through other hyperedges:
      (none)
  Member "convolutional dictionary learning a comparative review and new algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "convolutional dictionary learning based auto encoders for natural exponential family distributions")
  Member "latent space physics towards learning the temporal evolution of fluid flow":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "temporally folded convolutional neural networks for sequence forecasting")
  Member "continuous scale kinetic fluid simulation":
    also connected through other hyperedges:
      (none)
  Member "tempogan a temporally coherent volumetric gan for super resolution fluid flow":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "latent space subdivision stable and controllable time predictions for fluid flow")
  Member "amp inspired deep networks for sparse linear inverse problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "theoretical interpretation of learned step size in deep unfolded gradient descent")
  Member "liquid splash modeling with neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "latent space subdivision stable and controllable time predictions for fluid flow")

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)