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: "early methods for detecting adversarial images"
  Abstract: Many machine learning classifiers are vulnerable to adversarial perturbations. An adversarial perturbation modifies an input to change a classifier's prediction without causing the input to seem substantially different to human perception. We deploy three methods to detect adversarial images. Adversaries trying to bypass our detectors must make the adversarial image less pathological or they will fail trying. Our best detection method reveals that adversarial images place abnormal emphasis on the lower-ranked principal components from PCA. Other detectors and a colorful saliency map are in an appendix.

Connected through hyperedge HE-1 (induced by source paper "ghostimage perception domain attacks against vision based object classification systems"):
  with members:
    - "taxonomy and challenges of out of band signal injection attacks and defenses"
    - "adversarial sensor attack on lidar based perception in autonomous driving"
    - "making an invisibility cloak real world adversarial attacks on object detectors"
    - "are self driving cars secure evasion attacks against deep neural networks for steering angle prediction"
    - "advhat real world adversarial attack on arcface face id system"
    - "spatially transformed adversarial examples"
    - "stochastic activation pruning for robust adversarial defense"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "sentinet detecting localized universal attacks against deep learning systems"):
  with members:
    - "on adaptive attacks to adversarial example defenses"
    - "on evaluating adversarial robustness"
    - "model reuse attacks on deep learning systems"
    - "adversarial patch"
    - "yolov3 an incremental improvement"
    - "lavan localized and visible adversarial noise"
    - "anomaly detection using one class neural networks"
    - "review of deep learning"

Connected through hyperedge HE-3 (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-4 (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"
    - "magnet and efficient defenses against adversarial attacks are not robust to adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "dropcluster a structured dropout for convolutional networks"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "confidence calibration for convolutional neural networks using structured dropout"
    - "pytorch an imperative style high performance deep learning library"
    - "dropblock a regularization method for convolutional networks"
    - "open category detection with pac guarantees"
    - "feature grouping as a stochastic regularizer for high dimensional structured data"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "deep anomaly detection with outlier exposure"

Connected through hyperedge HE-6 (induced by source paper "adversarial examples opportunities and challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "atmpa attacking machine learning based malware visualization detection methods via adversarial examples"
    - "hcic hardware assisted control flow integrity checking"
    - "adversarial examples versus cloud based detectors a black box empirical study"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"
    - "adversarial attacks against medical deep learning systems"
    - "targeted adversarial examples for black box audio systems"

Connected through hyperedge HE-7 (induced by source paper "dla dense layer analysis for adversarial example detection"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial attacks and defences a survey"
    - "audio adversarial examples targeted attacks on speech to text"
    - "testing deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "adversarial learning in statistical classification a comprehensive review of defenses against attacks"):
  with members:
    - "revealing backdoors post training in dnn classifiers via novel inference on optimized perturbations inducing group misclassification"
    - "strip a defence against trojan attacks on deep neural networks"
    - "defensive quantization when efficiency meets robustness"
    - "revealing perceptible backdoors without the training set via the maximum achievable misclassification fraction statistic"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "a mixture model based defense for data poisoning attacks against naive bayes spam filters"

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)