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 "deep learning for determining a near optimal topological design without any iteration"):
  Abstract: In this study, we propose a novel deep learning-based method to predict an optimized structure for a given boundary condition and optimization setting without using any iterative scheme. For this purpose, first, using open-source topology optimization code, datasets of the optimized structures paired with the corresponding information on boundary conditions and optimization settings are generated at low (32 × 32) and high (128 × 128) resolutions. To construct the artificial neural network for the proposed method, a convolutional neural network (CNN)-based encoder and decoder network is trained using the training dataset generated at low resolution. Then, as a two-stage refinement, the conditional generative adversarial network (cGAN) is trained with the optimized structures paired at both low and high resolutions and is connected to the trained CNN-based encoder and decoder network. The performance evaluation results of the integrated network demonstrate that the proposed method can determine a near-optimal structure in terms of pixel values and compliance with negligible computational time.

Includes members:
  Member "neural networks for topology optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fea net a physics guided data driven model for efficient mechanical response prediction")
  Member "progressive growing of gans for improved quality stability and variation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "deep learning the physics of transport phenomena":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "encoding invariances in deep generative models")
  Member "stackgan realistic image synthesis with stacked generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "opengan open set generative adversarial networks")
  Member "tacotron towards end to end speech synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "semi supervised neural architecture search")
  Member "interactive 3d modeling with a generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fashion minimal edits for outfit improvement")
  Member "machine learning augmented predictive modeling of turbulent separated flows over airfoils":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning neural pde solvers with convergence guarantees")
  Member "photo realistic single image super resolution using a generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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)