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 generative design integration of topology optimization and generative models"):
  Abstract: Deep learning has recently been applied to various research areas of design optimization. This study presents the need and effectiveness of adopting deep learning for generative design (or design exploration) research area. This work proposes an artificial intelligent (AI)-based design automation framework that is capable of generating numerous design options which are not only aesthetic but also optimized for engineering performance. The proposed framework integrates topology optimization and deep generative models (e.g., generative adversarial networks (GANs)) in an iterative manner to explore new design options, thus generating a large number of designs starting from limited previous design data. In addition, anomaly detection can evaluate the novelty of generated designs, thus helping designers choose among design options. The 2D wheel design problem is applied as a case study for validation of the proposed framework. The framework manifests better aesthetics, diversity, and robustness of generated designs than previous generative design methods.

Includes members:
  Member "deep learning for determining a near optimal topological design without any iteration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "topologygan topology optimization using generative adversarial networks based on physical fields over the initial domain")
  Member "one shot generation of near optimal topology through theory driven machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fea net a physics guided data driven model for efficient mechanical response prediction")
  Member "3d topology optimization using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "machine learning accelerated topology optimization of nonlinear structures")
  Member "neural networks for topology optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fea net a physics guided data driven model for efficient mechanical response prediction")
  Member "deep learning the physics of transport phenomena":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "encoding invariances in deep generative models")
  Member "image quality assessment techniques show improved training and evaluation of autoencoder generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "high diversity attribute guided face generation with gans")
  Member "causalgan learning causal implicit generative models with adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "guided image generation with conditional invertible neural networks")
  Member "energy based 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)