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 "robustness assessment for adversarial machine learning problems solutions and a survey of current neural networks and defenses"):
  Abstract: In adversarial machine learning, there are a huge number of attacks of various types which evaluates robustness for new models and defences a daunting task. To make matters worse, there is an inherent bias in attacks and defences. Here, we organize the problems faced (model dependence, insufficient evaluation, false adversarial samples and perturbation dependent results) and propose a model agnostic dual ($L_0$ and $L_\infty$) quality assessment method together with the concept of robustness levels to tackle them. We validate the dual quality assessment on state-of-the-art models (WideResNet, ResNet, AllConv, DenseNet, NIN, LeNet and CapsNet) as well as the current hardest defences proposed at ICLR 2018 and the widely known adversarial training, showing that current models and defences are vulnerable in all levels of robustness. The robustness assessment show that depending on the metric used (i.e., $L_0$ or $L_\infty$) the robustness may change significantly and therefore duality should be taken into account for a correct assessment. Moreover, a mathematical derivation, as well as a counterexample, suggest that $L_1$ and $L_2$ metrics alone are not enough to avoid false adversarial samples. Interestingly, a by-product of the assessment proposed is a novel $L_\infty$ black-box method which requires even less perturbation than the One-Pixel Attack (only 12\% of One-Pixel Attack's amount of perturbation) to achieve similar results. Thus, this paper elucidates the problems of robustness evaluation, proposes a dual quality assessment to tackle them as well as survey the robustness of current models and defences. Code available at this http URL

Includes members:
  Member "one pixel attack for fooling deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial ranking attack and defense")
  Member "characterizing adversarial subspaces using local intrinsic dimensionality":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "feature squeezing detecting adversarial examples in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial ranking attack and defense")
  Member "adversarial examples are not easily detected bypassing ten detection methods":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "magnet and efficient defenses against adversarial attacks are not robust to adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "dynamic routing between capsules":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lightweight residual densely connected convolutional neural network")
  Member "countering adversarial images using input transformations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial ranking attack and defense")

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)