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 support vector machines in adversarial environments"
  Abstract: Support vector machines (SVMs) are among the most popular classification techniques adopted in security applications like malware detection, intrusion detection, and spam filtering. However, if SVMs are to be incorporated in real-world security systems, they must be able to cope with attack patterns that can either mislead the learning algorithm (poisoning), evade detection (evasion) or gain information about their internal parameters (privacy breaches). The main contributions of this chapter are twofold. First, we introduce a formal general framework for the empirical evaluation of the security of machine-learning systems. Second, according to our framework, we demonstrate the feasibility of evasion, poisoning and privacy attacks against SVMs in real-world security problems. For each attack technique, we evaluate its impact and discuss whether (and how) it can be countered through an adversary-aware design of SVMs. Our experiments are easily reproducible thanks to open-source code that we have made available, together with all the employed datasets, on a public repository.

Connected through hyperedge HE-1 (induced by source paper "adversarial security attacks and perturbations on machine learning and deep learning methods"):
  with members:
    - "on evaluating adversarial robustness"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "designing adversarially resilient classifiers using resilient feature engineering"
    - "security for machine learning based systems attacks and challenges during training and inference"
    - "security matters a survey on adversarial machine learning"
    - "security and privacy issues in deep learning"
    - "deep text classification can be fooled"
    - "mlcapsule guarded offline deployment of machine learning as a service"

Connected through hyperedge HE-2 (induced by source paper "the relationship between trust in ai and trustworthy machine learning technologies"):
  with members:
    - "decision provenance harnessing data flow for accountable systems"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "methods for interpreting and understanding deep neural networks"
    - "sok security and privacy in machine learning"
    - "yes machine learning can be more secure a case study on android malware detection"
    - "interpretable explorable approximations of black box models"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"

Connected through hyperedge HE-3 (induced by source paper "robustness verification of support vector machines"):
  with members:
    - "optimization and abstraction a synergistic approach for analyzing neural network robustness"
    - "formal security analysis of neural networks using symbolic intervals"
    - "adversarial framing for image and video classification"
    - "formal verification of piece wise linear feed forward neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "generating natural adversarial examples"
    - "safety verification of deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "optimal adversarial attack on autoregressive models"):
  with members:
    - "data poisoning against differentially private learners attacks and defenses"
    - "online data poisoning attack"
    - "an optimal control approach to sequential machine teaching"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "an optimal control view of adversarial machine learning"
    - "data poisoning attacks in contextual bandits"
    - "data poisoning attacks against online learning"
    - "crafting adversarial input sequences for recurrent neural networks"

Connected through hyperedge HE-5 (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-6 (induced by source paper "reinforcement learning for autonomous defence in software defined networking"):
  with members:
    - "semantic 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 attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "certified defenses for data poisoning attacks"
    - "enhancing robustness of machine learning systems via data transformations"

Connected through hyperedge HE-7 (induced by source paper "adversarial attacks and defences a survey"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "dolphinattack inaudible voice commands"
    - "magnet a two pronged defense against adversarial examples"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "extending defensive distillation"
    - "blocking transferability of adversarial examples in black box learning systems"

Connected through hyperedge HE-8 (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"

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)