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 "realtime simulation of thin shell deformable materials using cnn based mesh embedding"):
  Abstract: We address the problem of accelerating thin-shell deformable object simulations by dimension reduction. We present a new algorithm to embed a high-dimensional configuration space of deformable objects in a low-dimensional feature space, where the configurations of objects and feature points have approximate one-to-one mapping. Our key technique is a graph-based convolutional neural network (CNN) defined on meshes with arbitrary topologies and a new mesh embedding approach based on physics-inspired loss term. We have applied our approach to accelerate high-resolution thin shell simulations corresponding to cloth-like materials, where the configuration space has tens of thousands of degrees of freedom. We show that our physics-inspired embedding approach leads to higher accuracy compared with prior mesh embedding methods. Finally, we show that the temporal evolution of the mesh in the feature space can also be learned using a recurrent neural network (RNN) leading to fully learnable physics simulators. After training our learned simulator runs $10-100\times$ faster and the accuracy is high enough for robot manipulation tasks.

Includes members:
  Member "fast motion planning for high dof robot systems using hierarchical system identification":
    also connected through other hyperedges:
      (none)
  Member "hierarchical cloth simulation using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient cloth simulation using miniature cloth and upscaling deep neural networks")
  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 "latent space subdivision stable and controllable time predictions for fluid flow")
  Member "cloth manipulation using random forest based controller parametrization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cloth manipulation using random forest based imitation learning")
  Member "sparse data driven mesh deformation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rank3dgan semantic mesh generation using relative attributes")
  Member "variational autoencoders for deforming 3d mesh models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "learning complex dexterous manipulation with deep reinforcement learning and demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchically decoupled imitation for morphological transfer")

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)