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: "adversarial learning a critical review and active learning study"
  Abstract: This papers consists of two parts. The first is a critical review of prior art on adversarial learning, identifying some significant limitations of previous works. The second part is an experimental study considering adversarial active learning and an investigation of the efficacy of a mixed sample selection strategy for combating an adversary who attempts to disrupt the classifier learning.

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 "iot keeper securing iot communications in edge networks"):
  with members:
    - "sequence aggregation rules for anomaly detection in computer network traffic"
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "one class collective anomaly detection based on long short term memory recurrent neural networks"
    - "spying on the smart home privacy attacks and defenses on encrypted iot traffic"
    - "collective anomaly detection based on long short term memory recurrent neural network"
    - "detection of early stage enterprise infection by mining large scale log data"

Connected through hyperedge HE-3 (induced by source paper "when not to classify detection of reverse engineering attacks on dnn image classifiers"):
  with members:
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "when not to classify anomaly detection of attacks ada on dnn classifiers at test time"
    - "practical black box attacks against machine learning"
    - "stealing machine learning models via prediction apis"
    - "towards evaluating the robustness of neural networks"
    - "the limitations of deep learning in adversarial settings"

Connected through hyperedge HE-4 (induced by source paper "a mixture model based defense for data poisoning attacks against naive bayes spam filters"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "magnet a two pronged defense against adversarial examples"
    - "the limitations of deep learning in adversarial settings"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-5 (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"
    - "is deep learning safe for robot vision adversarial examples against the icub humanoid"
    - "targeted backdoor attacks on deep learning systems using data poisoning"

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.CR(Cryptography and Security)