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: "poisoning behavioral malware clustering"
  Abstract: Clustering algorithms have become a popular tool in computer security to analyze the behavior of malware variants, identify novel malware families, and generate signatures for antivirus systems. However, the suitability of clustering algorithms for security-sensitive settings has been recently questioned by showing that they can be significantly compromised if an attacker can exercise some control over the input data. In this paper, we revisit this problem by focusing on behavioral malware clustering approaches, and investigate whether and to what extent an attacker may be able to subvert these approaches through a careful injection of samples with poisoning behavior. To this end, we present a case study on Malheur, an open-source tool for behavioral malware clustering. Our experiments not only demonstrate that this tool is vulnerable to poisoning attacks, but also that it can be significantly compromised even if the attacker can only inject a very small percentage of attacks into the input data. As a remedy, we discuss possible countermeasures and highlight the need for more secure clustering algorithms.

Connected through hyperedge HE-1 (induced by source paper "the naked sun malicious cooperation between benign looking processes"):
  with members:
    - "black box attacks against rnn based malware detection algorithms"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "yes machine learning can be more secure a case study on android malware detection"
    - "hardening classifiers against evasion the good the bad and the ugly"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "adversarial detection of flash malware limitations and open issues"
    - "the limitations of deep learning in adversarial settings"
    - "evasion attacks against machine learning at test time"

Connected through hyperedge HE-2 (induced by source paper "deep probabilistic models to detect data poisoning attacks"):
  with members:
    - "strip a defence against trojan attacks on deep neural networks"
    - "moped efficient priors for scalable variational inference in bayesian deep neural networks"
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "wild patterns ten years after the rise of adversarial machine learning"):
  with members:
    - "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"
    - "yes machine learning can be more secure a case study on android malware detection"

Connected through hyperedge HE-4 (induced by source paper "sok security and privacy in machine learning"):
  with members:
    - "is feature selection secure against training data poisoning"
    - "adversarial attacks on neural network policies"
    - "certified defenses for data poisoning attacks"
    - "dropout inference in bayesian neural networks with alpha divergences"
    - "vulnerability of deep reinforcement learning to policy induction attacks"
    - "algorithmic decision making and the cost of fairness"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-5 (induced by source paper "certified defenses against adversarial examples"):
  with members:
    - "sok security and privacy in machine learning"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "towards proving the adversarial robustness of deep neural networks"
    - "robust physical world attacks on machine learning models"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "formal guarantees on the robustness of a classifier against adversarial manipulation"
    - "measuring neural net robustness with constraints"

Connected through hyperedge HE-6 (induced by source paper "analysis of location data leakage in the internet traffic of android based mobile devices"):
  with members:
    - "understanding tor usage with privacy preserving measurement"
    - "demo recon revealing and controlling pii leaks in mobile network traffic"
    - "powerspy location tracking using mobile device power analysis"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-7 (induced by source paper "certified defenses for data poisoning attacks"):
  with members:
    - "is feature selection secure against training data poisoning"
    - "is data clustering in adversarial settings secure"
    - "sok security and privacy in machine learning"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "generative poisoning attack method against neural networks"
    - "vulnerability of deep reinforcement learning to policy induction attacks"
    - "learning from untrusted data"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

Connected through hyperedge HE-8 (induced by source paper "towards poisoning of deep learning algorithms with back gradient optimization"):
  with members:
    - "is feature selection secure against training data poisoning"
    - "is data clustering in adversarial settings secure"
    - "certified defenses for data poisoning attacks"
    - "security evaluation of pattern classifiers under attack"
    - "practical black box attacks against machine learning"
    - "universal adversarial perturbations"
    - "automated dynamic analysis of ransomware benefits limitations and use for detection"
    - "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)