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: "the adversarial attack and detection under the fisher information metric"
  Abstract: Many deep learning models are vulnerable to the adversarial attack, i.e., imperceptible but intentionally-designed perturbations to the input can cause incorrect output of the networks. In this paper, using information geometry, we provide a reasonable explanation for the vulnerability of deep learning models. By considering the data space as a non-linear space with the Fisher information metric induced from a neural network, we first propose an adversarial attack algorithm termed one-step spectral attack (OSSA). The method is described by a constrained quadratic form of the Fisher information matrix, where the optimal adversarial perturbation is given by the first eigenvector, and the model vulnerability is reflected by the eigenvalues. The larger an eigenvalue is, the more vulnerable the model is to be attacked by the corresponding eigenvector. Taking advantage of the property, we also propose an adversarial detection method with the eigenvalues serving as characteristics. Both our attack and detection algorithms are numerically optimized to work efficiently on large datasets. Our evaluations show superior performance compared with other methods, implying that the Fisher information is a promising approach to investigate the adversarial attacks and defenses.

Connected through hyperedge HE-1 (induced by source paper "adversarial perturbations on the perceptual ball"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "understanding deep networks via extremal perturbations and smooth masks"
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "interpretable counterfactual explanations guided by prototypes"
    - "universal adversarial perturbations against person re identification"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "counterfactual visual explanations"
    - "hardening deep neural networks via adversarial model cascades"

Connected through hyperedge HE-2 (induced by source paper "smart skeletal motion action recognition attack"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "meshadv adversarial meshes for visual recognition"
    - "adversarial attacks on time series"
    - "adversarial attack on skeleton based human action recognition"
    - "enhancing adversarial example transferability with an intermediate level attack"
    - "spatio temporal manifold learning for human motions via long horizon modeling"
    - "adversarial attacks for optical flow based action recognition classifiers"
    - "spatially transformed adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "fisher bures adversary graph convolutional networks"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "simplifying graph convolutional networks"
    - "fisher information and natural gradient learning of random deep networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "pitfalls of graph neural network evaluation"
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"

Connected through hyperedge HE-4 (induced by source paper "inspecting adversarial examples using the fisher information"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "certified defenses against adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "efficient defenses against adversarial attacks"

Connected through hyperedge HE-5 (induced by source paper "adversarial embedding a robust and elusive steganography and watermarking technique"):
  with members:
    - "detection based defense against adversarial examples from the steganalysis point of view"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "hidden hiding data with deep networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "steganographic generative adversarial networks"
    - "on the statistical detection of adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "defending against adversarial attacks by suppressing the largest eigenvalue of fisher information matrix"):
  with members:
    - "when does label smoothing help"
    - "adversarial robustness via adversarial label smoothing"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "analysis of universal adversarial perturbations"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"
    - "adversarial machine learning at scale"
    - "the limitations of deep learning in adversarial settings"

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)