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: "magnet and efficient defenses against adversarial attacks are not robust to adversarial examples"
  Abstract: MagNet and "Efficient Defenses..." were recently proposed as a defense to adversarial examples. We find that we can construct adversarial examples that defeat these defenses with only a slight increase in distortion.

Connected through hyperedge HE-1 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence"):
  with members:
    - "towards ground truth evaluation of visual explanations"
    - "continual lifelong learning with neural networks a review"
    - "programmable neural network trojan for pre trained feature extractor"
    - "hidden trigger backdoor attacks"
    - "adversarial attacks and defenses in images graphs and text a review"
    - "on evaluating adversarial robustness"
    - "explainable ai for trees from local explanations to global understanding"
    - "wild patterns ten years after the rise of adversarial machine learning"

Connected through hyperedge HE-2 (induced by source paper "ensembles of many diverse weak defenses can be strong defending deep neural networks against adversarial attacks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "simple black box adversarial attacks"
    - "on evaluating adversarial robustness"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "enhancing robustness of machine learning systems via data transformations"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"

Connected through hyperedge HE-3 (induced by source paper "erase and restore simple accurate and resilient detection of l 2 adversarial examples"):
  with members:
    - "protecting jpeg images against adversarial attacks"
    - "adversarial robustness toolbox v1 0 0"
    - "deflecting adversarial attacks with pixel deflection"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "exploiting the inherent limitation of l0 adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-4 (induced by source paper "visionguard runtime detection of adversarial inputs to perception systems"):
  with members:
    - "detection based defense against adversarial examples from the steganalysis point of view"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial spheres"
    - "on the limitation of local intrinsic dimensionality for characterizing the subspaces of adversarial examples"
    - "lavan localized and visible adversarial noise"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-5 (induced by source paper "towards certifiable adversarial sample detection"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "certified adversarial robustness via randomized smoothing"
    - "gotta catch em all using concealed trapdoors to detect adversarial attacks on neural networks"
    - "sitatapatra blocking the transfer of adversarial samples"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "stateful detection of black box adversarial attacks"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"

Connected through hyperedge HE-6 (induced by source paper "minimax defense against gradient based adversarial attacks"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial robustness toolbox v1 0 0"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "magnet a two pronged defense against adversarial examples"
    - "ape gan adversarial perturbation elimination with gan"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "pixeldefend leveraging generative models to understand and defend against adversarial examples"

Connected through hyperedge HE-7 (induced by source paper "detecting and recovering adversarial examples an input sensitivity guided method"):
  with members:
    - "simple black box adversarial attacks"
    - "gradient similarity an explainable approach to detect adversarial attacks against deep learning"
    - "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"
    - "adversarial and clean data are not twins"
    - "magnet a two pronged defense against adversarial examples"
    - "countering adversarial images using input transformations"

Connected through hyperedge HE-8 (induced by source paper "towards robust dnns an taylor expansion based method for generating powerful adversarial examples"):
  with members:
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "practical black box attacks against machine learning"
    - "adversarial examples in the physical world"
    - "adversarial machine learning at scale"
    - "learning with a strong adversary"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "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)