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: "gradient masking causes clever to overestimate adversarial perturbation size"
  Abstract: A key problem in research on adversarial examples is that vulnerability to adversarial examples is usually measured by running attack algorithms. Because the attack algorithms are not optimal, the attack algorithms are prone to overestimating the size of perturbation needed to fool the target model. In other words, the attack-based methodology provides an upper-bound on the size of a perturbation that will fool the model, but security guarantees require a lower bound. CLEVER is a proposed scoring method to estimate a lower bound. Unfortunately, an estimate of a bound is not a bound. In this report, we show that gradient masking, a common problem that causes attack methodologies to provide only a very loose upper bound, causes CLEVER to overestimate the size of perturbation needed to fool the model. In other words, CLEVER does not resolve the key problem with the attack-based methodology, because it fails to provide a lower bound.

Connected through hyperedge HE-1 (induced by source paper "metrics and methods for robustness evaluation of neural networks with generative models"):
  with members:
    - "using videos to evaluate image model robustness"
    - "seeing what a gan cannot generate"
    - "adversarial examples are a natural consequence of test error in noise"
    - "natural adversarial examples"
    - "image synthesis with a single robust classifier"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "towards photographic image manipulation with balanced growing of generative autoencoders"
    - "optimization and abstraction a synergistic approach for analyzing neural network robustness"

Connected through hyperedge HE-2 (induced by source paper "robustness certificates against adversarial examples for relu networks"):
  with members:
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "are adversarial examples inevitable"
    - "black box adversarial attacks with limited queries and information"
    - "efficient neural network robustness certification with general activation functions"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "evaluating robustness of neural networks with mixed integer programming"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-3 (induced by source paper "testing robustness against unforeseen adversaries"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "adversarial examples are a natural consequence of test error in noise"
    - "natural adversarial examples"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "interpreting adversarially trained convolutional neural networks"
    - "exploiting excessive invariance caused by norm bounded adversarial robustness"
    - "sensitivity of deep convolutional networks to gabor noise"
    - "spatially transformed adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "global adversarial attacks for assessing deep learning robustness"):
  with members:
    - "generating realistic unrestricted adversarial inputs using dual objective gan training"
    - "limitations of the lipschitz constant as a defense against adversarial examples"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "constructing unrestricted adversarial examples with generative models"
    - "analysis of universal adversarial perturbations"
    - "deep learning in the automotive industry applications and tools"
    - "towards evaluating the robustness of neural networks"
    - "adversarial machine learning at scale"

Connected through hyperedge HE-5 (induced by source paper "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "towards proving the adversarial robustness of deep neural networks"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"

Connected through hyperedge HE-6 (induced by source paper "an efficient and margin approaching zero confidence adversarial attack"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "black box adversarial attacks with limited queries and information"
    - "distributionally adversarial attack"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "practical black box attacks against machine learning"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"
    - "adversarial machine learning at scale"

Connected through hyperedge HE-7 (induced by source paper "on extensions of clever a neural network robustness evaluation algorithm"):
  with members:
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "magnet and efficient defenses against adversarial attacks are not robust to adversarial examples"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "countering adversarial images using input transformations"
    - "formal guarantees on the robustness of a classifier against adversarial manipulation"
    - "measuring neural net robustness with constraints"

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)