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: "security evaluation of pattern classifiers under attack"
  Abstract: Pattern classification systems are commonly used in adversarial applications, like biometric authentication, network intrusion detection, and spam filtering, in which data can be purposely manipulated by humans to undermine their operation. As this adversarial scenario is not taken into account by classical design methods, pattern classification systems may exhibit vulnerabilities, whose exploitation may severely affect their performance, and consequently limit their practical utility. Extending pattern classification theory and design methods to adversarial settings is thus a novel and very relevant research direction, which has not yet been pursued in a systematic way. In this paper, we address one of the main open issues: evaluating at design phase the security of pattern classifiers, namely, the performance degradation under potential attacks they may incur during operation. We propose a framework for empirical evaluation of classifier security that formalizes and generalizes the main ideas proposed in the literature, and give examples of its use in three real applications. Reported results show that security evaluation can provide a more complete understanding of the classifier's behavior in adversarial environments, and lead to better design choices.

Connected through hyperedge HE-1 (induced by source paper "fault sneaking attack a stealthy framework for misleading deep neural networks"):
  with members:
    - "defensive dropout for hardening deep neural networks under adversarial attacks"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "is feature selection secure against training data poisoning"
    - "an admm based universal framework for adversarial attacks on deep neural networks"
    - "efficient neural network robustness certification with general activation functions"
    - "robust physical world attacks on deep learning models"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "yes machine learning can be more secure a case study on android malware detection"

Connected through hyperedge HE-2 (induced by source paper "wild patterns ten years after the rise of adversarial machine learning"):
  with members:
    - "poisoning behavioral malware clustering"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "is feature selection secure against training data poisoning"
    - "is data clustering in adversarial settings secure"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "certified defenses for data poisoning attacks"
    - "deltaphish detecting phishing webpages in compromised websites"

Connected through hyperedge HE-3 (induced by source paper "towards adversarial malware detection lessons learned from pdf based attacks"):
  with members:
    - "explaining vulnerabilities of deep learning to adversarial malware binaries"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "model reuse attacks on deep learning systems"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "explaining black box android malware detection"
    - "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "adversarial attacks on node embeddings via graph poisoning"):
  with members:
    - "adversarial attacks on graph neural networks via meta learning"
    - "certifiable robustness and robust training for graph convolutional networks"
    - "adversarial attack on graph structured data"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "deep text classification can be fooled"
    - "towards crafting text adversarial samples"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "disabling external influence in social networks via edge recommendation"

Connected through hyperedge HE-5 (induced by source paper "adversarial robustness toolbox v1 0 0"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "robustness verification of tree based models"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "the limitations of model uncertainty in adversarial settings"
    - "sok security and privacy in machine learning"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "magnet a two pronged defense against adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "a dynamic adversarial mining approach to the security of machine learning"):
  with members:
    - "handling adversarial concept drift in streaming data"
    - "sok security and privacy in machine learning"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "stealing machine learning models via prediction apis"
    - "the limitations of deep learning in adversarial settings"
    - "evasion attacks against machine learning at test time"

Connected through hyperedge HE-7 (induced by source paper "handling adversarial concept drift in streaming data"):
  with members:
    - "sok security and privacy in machine learning"
    - "security theater on the vulnerability of classifiers to exploratory attacks"
    - "fraternal twins unifying attacks on machine learning and digital watermarking"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "stealing machine learning models via prediction apis"
    - "evasion attacks against machine learning at test time"
    - "scikit learn machine learning in python"
    - "learning under concept drift an overview"

Connected through hyperedge HE-8 (induced by source paper "attacking automatic video analysis algorithms a case study of google cloud video intelligence api"):
  with members:
    - "sok security and privacy in machine learning"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "deep learning for video classification and captioning"
    - "practical black box attacks against machine learning"
    - "concrete problems in ai safety"
    - "stealing machine learning models via prediction apis"
    - "convolutional two stream network fusion for video action recognition"
    - "membership inference attacks against machine learning models"

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)