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: "is data clustering in adversarial settings secure"
  Abstract: Clustering algorithms have been increasingly adopted in security applications to spot dangerous or illicit activities. However, they have not been originally devised to deal with deliberate attack attempts that may aim to subvert the clustering process itself. Whether clustering can be safely adopted in such settings remains thus questionable. In this work we propose a general framework that allows one to identify potential attacks against clustering algorithms, and to evaluate their impact, by making specific assumptions on the adversary's goal, knowledge of the attacked system, and capabilities of manipulating the input data. We show that an attacker may significantly poison the whole clustering process by adding a relatively small percentage of attack samples to the input data, and that some attack samples may be obfuscated to be hidden within some existing clusters. We present a case study on single-linkage hierarchical clustering, and report experiments on clustering of malware samples and handwritten digits.

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 "a backdoor attack against lstm based text classification systems"):
  with members:
    - "design of intentional backdoors in sequential models"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "spectral signatures in backdoor attacks"
    - "how to backdoor federated learning"
    - "generative poisoning attack method against neural networks"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "targeted backdoor attacks on deep learning systems using data poisoning"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-3 (induced by source paper "suspicion free adversarial attacks on clustering algorithms"):
  with members:
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "blackbox a procedure for parallel optimization of expensive black box functions"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "are you tampering with my data"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "laplacian networks bounding indicator function smoothness for neural networks robustness"
    - "deepdiva a highly functional python framework for reproducible experiments"
    - "is feature selection secure against training data poisoning"
    - "potrojan powerful neural level trojan designs in deep learning models"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "peernets exploiting peer wisdom against adversarial attacks"
    - "towards an understanding of neural networks in natural image spaces"

Connected through hyperedge HE-6 (induced by source paper "poisoning behavioral malware clustering"):
  with members:
    - "security evaluation of support vector machines in adversarial environments"
    - "evasion attacks against machine learning at test time"
    - "poisoning attacks against support vector machines"

Connected through hyperedge HE-7 (induced by source paper "is feature selection secure against training data poisoning"):
  with members:
    - "evasion attacks against machine learning at test time"
    - "poisoning attacks against support vector machines"

Connected through hyperedge HE-8 (induced by source paper "certified defenses for data poisoning attacks"):
  with members:
    - "poisoning behavioral malware clustering"
    - "is feature selection secure against training data poisoning"
    - "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"

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)