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: "model agnostic dual quality assessment for adversarial machine learning and an analysis of current neural networks and defenses"
  Abstract: In adversarial machine learning, there are a huge number of attacks of various types which makes the evaluation of robustness for new models and defenses a daunting task. To make matters worse, there is an inherent bias in attacks and defenses. Here, we organize the problems faced (model dependence, insufficient evaluation, unreliable adversarial samples and perturbation dependent results) and propose a dual 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 defenses proposed at ICLR 2018 as well as the widely known adversarial training, showing that current models and defenses are vulnerable in all levels of robustness. Moreover, we show that robustness to $L_0$ and $L_\infty$ attacks differ greatly and therefore duality should be taken into account for a correct assessment. 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 analyze the robustness of current models and defenses. Hopefully, the current analysis and proposed methods would aid the development of more robust deep neural networks and hybrids alike. #R##N#Code available at: this http URL

Connected through hyperedge HE-1 (induced by source paper "evolving robust neural architectures to defend from adversarial attacks"):
  with members:
    - "evolving deep neural networks by multi objective particle swarm optimization for image classification"
    - "one pixel attack for fooling deep neural networks"
    - "evolutionary neural automl for deep learning"
    - "understanding the one pixel attack propagation maps and locality analysis"
    - "adversarial patch"
    - "evolutionary architecture search for deep multitask networks"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-2 (induced by source paper "uncovering why deep neural networks lack robustness representation metrics that link to adversarial attacks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "understanding the one pixel attack propagation maps and locality analysis"
    - "adversarial patch"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "dynamic routing between capsules"
    - "pixeldefend leveraging generative models to understand and defend against adversarial examples"
    - "synthesizing robust adversarial examples"

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)