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: "understanding adversarial robustness the trade off between minimum and average margin"
  Abstract: Deep models, while being extremely versatile and accurate, are vulnerable to adversarial attacks: slight perturbations that are imperceptible to humans can completely flip the prediction of deep models. Many attack and defense mechanisms have been proposed, although a satisfying solution still largely remains elusive. In this work, we give strong evidence that during training, deep models maximize the minimum margin in order to achieve high accuracy, but at the same time decrease the \emph{average} margin hence hurting robustness. Our empirical results highlight an intrinsic trade-off between accuracy and robustness for current deep model training. To further address this issue, we propose a new regularizer to explicitly promote average margin, and we verify through extensive experiments that it does lead to better robustness. Our regularized objective remains Fisher-consistent, hence asymptotically can still recover the Bayes optimal classifier.

Connected through hyperedge HE-1 (induced by source paper "a useful taxonomy for adversarial robustness of neural networks"):
  with members:
    - "adversarial defense by suppressing high frequency components"
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "metric learning for adversarial robustness"
    - "adversarial robustness may be at odds with simplicity"
    - "are labels required for improving adversarial robustness"
    - "batch normalization is a cause of adversarial vulnerability"
    - "towards understanding adversarial examples systematically exploring data size task and model factors"
    - "adversarial attacks and defenses in images graphs and text a review"

Connected through hyperedge HE-2 (induced by source paper "robust deep neural networks inspired by fuzzy logic"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "adversarial examples are a natural consequence of test error in noise"
    - "a kernelized manifold mapping to diminish the effect of adversarial perturbations"
    - "adversarial training versus weight decay"
    - "sparse dnns with improved adversarial robustness"
    - "neural networks should be wide enough to learn disconnected decision regions"
    - "adversarially robust generalization requires more data"
    - "provable robustness of relu networks via maximization of linear regions"

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)