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 "efficient cloth simulation using miniature cloth and upscaling deep neural networks"):
  Abstract: Cloth simulation requires a fast and stable method for interactively and realistically visualizing fabric materials using computer graphics. We propose an efficient cloth simulation method using miniature cloth simulation and upscaling Deep Neural Networks (DNN). The upscaling DNNs generate the target cloth simulation from the results of physically-based simulations of a miniature cloth that has similar physical properties to those of the target cloth. We have verified the utility of the proposed method through experiments, and the results demonstrate that it is possible to generate fast and stable cloth simulations under various conditions.

Includes members:
  Member "nnwarp neural network based nonlinear deformation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "simulation of hyperelastic materials in real time using deep learning")
  Member "anderson acceleration for geometry optimization and physics simulation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "differentiable fixed point iteration layer")
  Member "hierarchical cloth simulation using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "realtime simulation of thin shell deformable materials using cnn based mesh embedding")
  Member "latent space physics towards learning the temporal evolution of fluid flow":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "latent space subdivision stable and controllable time predictions for fluid flow")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  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 "a compositional object based approach to learning physical dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")

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)