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 "toward finding the global optimal of adversarial examples"):
  Abstract: Current machine learning models are vulnerable to adversarial examples (Goodfellow et al., 2014), we noticed that current state-of-the-art methods (Kurakin et al., 2016; Cheng et al., 2018) to attack a well-trained model often stuck in local optimal values. We conduct series of experiments on both white-box and black-box settings, and find out that by different initialization, the attack algorithm will finally converge to very different local optimals, suggesting the importance of careful and thorough search in the attack space. In this paper, we propose a general boosting algorithm that can help current attack to find a more global optimal example. Specifically, we search for the adversarial examples by starting from different points/directions, and in certain interval we adopt successive halving (Jamieson & Talwalkar, 2016) to cut down the searching directions that are not promising, and use Bayesian Optimization (Pelikan et al., 1999; Bergstra et al., 2011) to resample from the search space based on the knowledge obtained from past searches. We demonstrate that by applying our methods to state-of-the-art attack algorithms in both black-and white box setting, we can further reduce the distortion between the original image and adversarial sample about 10%-20%. By adopting dynamic successive halving, we can reduce the computation cost 5-10 times without harming the final result. We conduct experiments in models trained on MNIST or ImageNet and also try on decision tree models, these experiments suggest that our method is a general way to boost the performance of current adversarial attack methods.

Includes members:
  Member "sign opt a query efficient hard label adversarial attack":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cat customized adversarial training for improved robustness")
  Member "hopskipjumpattack a query efficient decision based attack":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial ranking attack and defense")
  Member "robust decision trees against adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robustness for non parametric classification a generic attack and defense")
  Member "enhancing gradient based attacks with symbolic intervals":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "certified defenses for adversarial patches")
  Member "ml loo detecting adversarial examples with feature attribution":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "interpreting adversarial examples by activation promotion and suppression")
  Member "characterizing adversarial subspaces using local intrinsic dimensionality":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "mixtrain scalable training of formally robust neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust deep reinforcement learning against adversarial perturbations on observations")
  Member "genattack practical black box attacks with gradient free optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "one man s trash is another man s treasure resisting adversarial examples by 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.AI(Artificial Intelligence)