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: "beyond adversarial training min max optimization in adversarial attack and defense"
  Abstract: The worst-case training principle that minimizes the maximal adversarial loss, also known as adversarial training (AT), has shown to be a state-of-the-art approach for enhancing adversarial robustness against norm-ball bounded input perturbations. Nonetheless, min-max optimization beyond the purpose of AT has not been rigorously explored in the research of adversarial attack and defense. In particular, given a set of risk sources (domains), minimizing the maximal loss induced from the domain set can be reformulated as a general min-max problem that is different from AT, since the maximization is taken over the probability simplex of the domain set. Examples of this general formulation include attacking model ensembles, devising universal perturbation to input samples or data transformations, and generalized AT over multiple norm-ball threat models. We show that these problems can be solved under a unified and theoretically principled min-max optimization framework. Our proposed approach leads to substantial performance improvement over the uniform averaging strategy in four different tasks. Moreover, we show how the self-adjusted weighting factors of the probability simplex from our proposed algorithms can be used to explain the importance of different attack and defense models.

Connected through hyperedge HE-1 (induced by source paper "adversarial t shirt evading person detectors in a physical world"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "adversarial objects against lidar based autonomous driving systems"
    - "on the sensitivity of adversarial robustness to input data distributions"
    - "adversarial camera stickers a physical camera based attack on deep learning systems"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "structured adversarial attack towards general implementation and better interpretability"

Connected through hyperedge HE-2 (induced by source paper "min max optimization without gradients convergence and applications to adversarial ml"):
  with members:
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "on the convergence and robustness of training gans with regularized optimal transport"
    - "robust optimization over multiple domains"
    - "spectral signatures in backdoor attacks"
    - "caad 2018 iterative ensemble adversarial attack"
    - "approximating nash equilibria for black box games a bayesian optimization approach"
    - "towards distributed coevolutionary gans"
    - "certified defenses for data poisoning attacks"

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)