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 "on the design of black box adversarial examples by leveraging gradient free optimization and operator splitting method"):
  Abstract: Robust machine learning is currently one of the most prominent topics which could potentially help shaping a future of advanced AI platforms that not only perform well in average cases but also in worst cases or adverse situations. Despite the long-term vision, however, existing studies on black-box adversarial attacks are still restricted to very specific settings of threat models (e.g., single distortion metric and restrictive assumption on target model's feedback to queries) and/or suffer from prohibitively high query complexity. To push for further advances in this field, we introduce a general framework based on an operator splitting method, the alternating direction method of multipliers (ADMM) to devise efficient, robust black-box attacks that work with various distortion metrics and feedback settings without incurring high query complexity. Due to the black-box nature of the threat model, the proposed ADMM solution framework is integrated with zeroth-order (ZO) optimization and Bayesian optimization (BO), and thus is applicable to the gradient-free regime. This results in two new black-box adversarial attack generation methods, ZO-ADMM and BO-ADMM. Our empirical evaluations on image classification datasets show that our proposed approaches have much lower function query complexities compared to state-of-the-art attack methods, but achieve very competitive attack success rates.

Includes members:
  Member "topology attack and defense for graph neural networks an optimization perspective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "defensive quantization when efficiency meets robustness":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "triple wins boosting accuracy robustness and efficiency together by enabling input adaptive inference")
  Member "fault sneaking attack a stealthy framework for misleading deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards query efficient black box adversary with zeroth order natural gradient descent")
  Member "interpreting adversarial examples by activation promotion and suppression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "heat and blur an effective and fast defense against adversarial examples")
  Member "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey of black box adversarial attacks on computer vision models")
  Member "defensive dropout for hardening deep neural networks under adversarial attacks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards query efficient black box adversary with zeroth order natural gradient descent")
  Member "adversarial generative nets neural network attacks on state of the art face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial machine learning industry perspectives")
  Member "exploring the space of black box attacks on deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "at gan an adversarial generator model for non constrained adversarial examples")

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.CV(Computer Vision and Pattern Recognition)