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 "sorting out lipschitz function approximation"):
  Abstract: Training neural networks subject to a Lipschitz constraint is useful for generalization bounds, provable adversarial robustness, interpretable gradients, and Wasserstein distance estimation. By the composition property of Lipschitz functions, it suffices to ensure that each individual affine transformation or nonlinear activation function is 1-Lipschitz. The challenge is to do this while maintaining the expressive power. We identify a necessary property for such an architecture: each of the layers must preserve the gradient norm during backpropagation. Based on this, we propose to combine a gradient norm preserving activation function, GroupSort, with norm-constrained weight matrices. We show that norm-constrained GroupSort architectures are universal Lipschitz function approximators. Empirically, we show that norm-constrained GroupSort networks achieve tighter estimates of Wasserstein distance than their ReLU counterparts and can achieve provable adversarial robustness guarantees with little cost to accuracy.

Includes members:
  Member "limitations of the lipschitz constant as a defense against adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial robustness through local lipschitzness")
  Member "the singular values of convolutional layers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding generalization in deep learning via tensor methods")
  Member "aggregated momentum stability through passive damping":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gradient descent with momentum to accelerate or to super accelerate")
  Member "lipschitz margin training scalable certification of perturbation invariance for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "varmixup exploiting the latent space for robust training and inference")
  Member "spectral normalization for generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "on convergence and stability of gans":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "blur noise and compression robust generative adversarial networks")
  Member "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a comprehensive and modularized statistical framework for gradient norm equality in deep neural networks")
  Member "exploring generalization in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "overfitting in adversarially robust deep 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.LG(Machine Learning)