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: "assessing threat of adversarial examples on deep neural networks"
  Abstract: Deep neural networks are facing a potential security threat from adversarial examples, inputs that look normal but cause an incorrect classification by the deep neural network. For example, the proposed threat could result in hand-written digits on a scanned check being incorrectly classified but looking normal when humans see them. This research assesses the extent to which adversarial examples pose a security threat, when one considers the normal image acquisition process. This process is mimicked by simulating the transformations that normally occur in acquiring the image in a real world application, such as using a scanner to acquire digits for a check amount or using a camera in an autonomous car. These small transformations negate the effect of the carefully crafted perturbations of adversarial examples, resulting in a correct classification by the deep neural network. Thus just acquiring the image decreases the potential impact of the proposed security threat. We also show that the already widely used process of averaging over multiple crops neutralizes most adversarial examples. Normal preprocessing, such as text binarization, almost completely neutralizes adversarial examples. This is the first paper to show that for text driven classification, adversarial examples are an academic curiosity, not a security threat.

Connected through hyperedge HE-1 (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"

Connected through hyperedge HE-2 (induced by source paper "convolutional neural networks with transformed input based on robust tensor network decomposition"):
  with members:
    - "interpretable convolutional neural networks via feedforward design"
    - "spatially transformed adversarial examples"
    - "sok security and privacy in machine learning"
    - "robustness of rotation equivariant networks to adversarial perturbations"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "on data driven saak transform"

Connected through hyperedge HE-3 (induced by source paper "adversarial attacks against medical deep learning systems"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "a dual approach to scalable verification of deep networks"
    - "adversarial patch"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "adversarial logit pairing"
    - "generalizability vs robustness adversarial examples for medical imaging"
    - "certified defenses against adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "noise flooding for detecting audio adversarial examples against automatic speech recognition"):
  with members:
    - "did you hear that adversarial examples against automatic speech recognition"
    - "isolated and ensemble audio preprocessing methods for detecting adversarial examples against automatic speech recognition"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "the effects of jpeg and jpeg2000 compression on attacks using adversarial examples"
    - "deflecting adversarial attacks with pixel deflection"
    - "audio adversarial examples targeted attacks on speech to text"
    - "adversarial example defenses ensembles of weak defenses are not strong"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-5 (induced by source paper "isolated and ensemble audio preprocessing methods for detecting adversarial examples against automatic speech recognition"):
  with members:
    - "did you hear that adversarial examples against automatic speech recognition"
    - "deflecting adversarial attacks with pixel deflection"
    - "audio adversarial examples targeted attacks on speech to text"
    - "adversarial example defenses ensembles of weak defenses are not strong"
    - "speex a free codec for free speech"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-6 (induced by source paper "defense against adversarial attacks using high level representation guided denoiser"):
  with members:
    - "sok security and privacy in machine learning"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "magnet a two pronged defense against adversarial examples"
    - "adversarial transformation networks learning to generate adversarial examples"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "perceptual losses for real time style transfer and super resolution"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-7 (induced by source paper "blocking transferability of adversarial examples in black box learning systems"):
  with members:
    - "sok security and privacy in machine learning"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "suppressing the unusual towards robust cnns using symmetric activation functions"
    - "practical black box attacks against machine learning"
    - "towards robust deep neural networks with bang"
    - "adversary resistant deep neural networks with an application to malware detection"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

Connected through hyperedge HE-8 (induced by source paper "when not to classify anomaly detection of attacks ada on dnn classifiers at test time"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "yes machine learning can be more secure a case study on android malware detection"
    - "magnet a two pronged defense against adversarial examples"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "efficient defenses against adversarial attacks"
    - "adversarial learning a critical review and active learning study"
    - "is deep learning safe for robot vision adversarial examples against the icub humanoid"

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.CV(Computer Vision and Pattern Recognition)