human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "generalizable adversarial training via spectral normalization"
  Abstract: Deep neural networks (DNNs) have set benchmarks on a wide array of supervised learning tasks. Trained DNNs, however, often lack robustness to minor adversarial perturbations to the input, which undermines their true practicality. Recent works have increased the robustness of DNNs by fitting networks using adversarially-perturbed training samples, but the improved performance can still be far below the performance seen in non-adversarial settings. A significant portion of this gap can be attributed to the decrease in generalization performance due to adversarial training. In this work, we extend the notion of margin loss to adversarial settings and bound the generalization error for DNNs trained under several well-known gradient-based attack schemes, motivating an effective regularization scheme based on spectral normalization of the DNN's weight matrices. We also provide a computationally-efficient method for normalizing the spectral norm of convolutional layers with arbitrary stride and padding schemes in deep convolutional networks. We evaluate the power of spectral normalization extensively on combinations of datasets, network architectures, and adversarial training schemes. The code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "gans may have no nash equilibria"):
  with members:
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "lipschitz generative adversarial nets"
    - "on gradient descent ascent for nonconvex concave minimax problems"
    - "policy optimization provably converges to nash equilibria in zero sum linear quadratic games"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "sgd learns one layer networks in wgans"
    - "on solving minimax optimization locally a follow the ridge approach"
    - "convergence of learning dynamics in stackelberg games"

Connected through hyperedge HE-2 (induced by source paper "adversarial training is a form of data dependent operator norm regularization"):
  with members:
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "adversarial vulnerability for any classifier"
    - "spectral normalization for generative adversarial networks"
    - "certified defenses against adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "generalised lipschitz regularisation equals distributional robustness"):
  with members:
    - "naive feature selection sparsity in naive bayes"
    - "sorting out lipschitz function approximation"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "spectral normalization for generative adversarial networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "universal adversarial perturbations"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"

Connected through hyperedge HE-4 (induced by source paper "high frequency component helps explain the generalization of convolutional neural networks"):
  with members:
    - "on the effectiveness of low frequency perturbations"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "theoretically principled trade off between robustness and accuracy"
    - "batch normalization is a cause of adversarial vulnerability"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "learning robust representations by projecting superficial statistics out"
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"

Connected through hyperedge HE-5 (induced by source paper "adversarial training generalizes data dependent spectral norm regularization"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "adversarial spheres"
    - "adversarial vulnerability for any classifier"
    - "spectral normalization for generative adversarial networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "adversarially robust generalization requires more data"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "robust design of deep neural networks against adversarial attacks based on lyapunov theory"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "certified adversarial robustness via randomized smoothing"
    - "the coupling effect of lipschitz regularization in deep neural networks"
    - "connecting lyapunov control theory to adversarial attacks"
    - "stability certified reinforcement learning a control theoretic perspective"
    - "the singular values of convolutional layers"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "l2 nonexpansive neural networks"

Connected through hyperedge HE-7 (induced by source paper "adversarial risk bounds for neural networks through sparsity based compression"):
  with members:
    - "vc classes are adversarially robustly learnable but only improperly"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "adversarial risk and robustness general definitions and implications for the uniform distribution"
    - "improved generalization bounds for robust learning"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "on tighter generalization bound for deep neural networks cnns resnets and beyond"
    - "exploring generalization in deep learning"

Connected through hyperedge HE-8 (induced by source paper "mean spectral normalization of deep neural networks for embedded automation"):
  with members:
    - "deep learning algorithms for bearing fault diagnosticsx a comprehensive review"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "spectral normalization for generative adversarial networks"
    - "understanding batch normalization"
    - "generative adversarial network based autoencoder application to fault detection problem for closed loop dynamical systems"
    - "generative adversarial learning for spectrum sensing"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "exploring generalization in deep learning"

Question: Please tell me which class the center node 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)