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: "detecting cyberattacks in industrial control systems using convolutional neural networks"
  Abstract: This paper presents a study on detecting cyberattacks on industrial control systems (ICS) using unsupervised deep neural networks, specifically, convolutional neural networks. The study was performed on a SecureWater Treatment testbed (SWaT) dataset, which represents a scaled-down version of a real-world industrial water treatment plant. e suggest a method for anomaly detection based on measuring the statistical deviation of the predicted value from the observed value.We applied the proposed method by using a variety of deep neural networks architectures including different variants of convolutional and recurrent networks. The test dataset from SWaT included 36 different cyberattacks. The proposed method successfully detects the vast majority of the attacks with a low false positive rate thus improving on previous works based on this data set. The results of the study show that 1D convolutional networks can be successfully applied to anomaly detection in industrial control systems and outperform more complex recurrent networks while being much smaller and faster to train.

Connected through hyperedge HE-1 (induced by source paper "the threat of adversarial attacks on machine learning in network security a survey"):
  with members:
    - "improving network intrusion detection classiﬁers by non payload based exploit independent obfuscations an adversarial approach"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "an adaptive system for detecting malicious queries in web attacks"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "exploring adversarial examples in malware detection"
    - "is feature selection secure against training data poisoning"
    - "adversarially robust generalization requires more data"
    - "idsgan generative adversarial networks for attack generation against intrusion detection"

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)