human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "efficient cyber attacks detection in industrial control systems using lightweight neural networks"):
  Abstract: Industrial control systems (ICSs) are widely used and vital to industry and society. Their failure can have severe impact on both economics and human life. Hence, these systems have become an attractive target for attacks, both physical and cyber. A number of attacks detection methods were proposed, however, they are characterized by an insufficient detection rate, a substantial false positives rate, or are system specific. In this paper, we study an attack detection method based on simple and lightweight neural networks, namely, 1D convolutions and autoencoders. We apply these networks to both time and frequency domains of the collected data and discuss pros and cons of each approach. We evaluate the suggested method on three popular public datasets and achieve detection metrics matching or exceeding previously published detection results, while featuring small footprint, short training and detection times, and generality.

Includes members:
  Member "real time evasion attacks with physical constraints on deep learning based anomaly detectors in industrial control systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "can t boil this frog robustness of online trained autoencoder based anomaly detectors to adversarial poisoning attacks")
  Member "mad gan multivariate anomaly detection for time series data with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "wild patterns ten years after the rise of adversarial machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "cyberattack detection using deep generative models with variational inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "enhanced variational inference with dyadic transformation")
  Member "kitsune an ensemble of autoencoders for online network intrusion detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "text a 3 activation anomaly analysis")
  Member "adversarial regression for detecting attacks in cyber physical systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "conaml constrained adversarial machine learning for cyber physical systems")
  Member "a deep learning based framework for conducting stealthy attacks in industrial control systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "conaml constrained adversarial machine learning for cyber physical systems")
  Member "anomaly detection for a water treatment system using unsupervised machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "conaml constrained adversarial machine learning for cyber physical systems")

Question: Please tell me which class the source paper 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)