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 "padgan a generative adversarial network for performance augmented diverse designs"):
  Abstract: Deep generative models are proven to be a useful tool for automatic design synthesis and design space exploration. When applied in engineering design, existing generative models face two challenges: 1) generated designs lack diversity and do not cover all areas of the design space and 2) it is difficult to explicitly improve the overall performance or quality of generated designs without excluding low-quality designs from the dataset, which may impair the performance of the trained model due to reduced training sample size. In this paper, we simultaneously address these challenges by proposing a new Determinantal Point Processes based loss function for probabilistic modeling of diversity and quality. With this new loss function, we develop a variant of the Generative Adversarial Network, named "Performance Augmented Diverse Generative Adversarial Network" or PaDGAN, which can generate novel high-quality designs with good coverage of the design space. We demonstrate that PaDGAN can generate diverse and high-quality designs on both synthetic and real-world examples and compare PaDGAN against other models such as the vanilla GAN and the BezierGAN. Unlike typical generative models that usually generate new designs by interpolating within the boundary of training data, we show that PaDGAN expands the design space boundary towards high-quality regions. The proposed method is broadly applicable to many tasks including design space exploration, design optimization, and creative solution recommendation.

Includes members:
  Member "design and evaluation of product aesthetics a human machine hybrid approach":
    also connected through other hyperedges:
      (none)
  Member "deep generative design integration of topology optimization and generative models":
    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 "automatic chemical design using a data driven continuous representation of molecules":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")
  Member "beziergan automatic generation of smooth curves from interpretable low dimensional parameters":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "3d shape synthesis for conceptual design and optimization using variational autoencoders")
  Member "mggan solving mode collapse using manifold guided training":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dualdis dual branch disentangling with adversarial learning")
  Member "gdpp learning diverse generations using determinantal point process":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "best of many samples distribution matching")
  Member "ranking ideas for diversity and quality":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "forming diverse teams from sequentially arriving people")
  Member "improved techniques for training gans":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)