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 "min max optimization without gradients convergence and applications to adversarial ml"):
  Abstract: In this paper, we study the problem of constrained robust (min-max) optimization ina black-box setting, where the desired optimizer cannot access the gradients of the objective function but may query its values. We present a principled optimization framework, integrating a zeroth-order (ZO) gradient estimator with an alternating projected stochastic gradient descent-ascent method, where the former only requires a small number of function queries and the later needs just one-step descent/ascent update. We show that the proposed framework, referred to as ZO-Min-Max, has a sub-linear convergence rate under mild conditions and scales gracefully with problem size. From an application side, we explore a promising connection between black-box min-max optimization and black-box evasion and poisoning attacks in adversarial machine learning (ML). Our empirical evaluations on these use cases demonstrate the effectiveness of our approach and its scalability to dimensions that prohibit using recent black-box solvers.

Includes members:
  Member "beyond adversarial training min max optimization in adversarial attack and defense":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial t shirt evading person detectors in a physical world")
  Member "manipulating machine learning poisoning attacks and countermeasures for regression learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "on the convergence and robustness of training gans with regularized optimal transport":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gans may have no nash equilibria")
  Member "robust optimization over multiple domains":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distribution agnostic model agnostic meta learning")
  Member "spectral signatures in backdoor attacks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "caad 2018 iterative ensemble adversarial attack":
    also connected through other hyperedges:
      (none)
  Member "approximating nash equilibria for black box games a bayesian optimization approach":
    also connected through other hyperedges:
      (none)
  Member "towards distributed coevolutionary gans":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "coegan evaluating the coevolution effect in generative adversarial networks")

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)