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 "intrusion detection for industrial control systems evaluation analysis and adversarial attacks"):
  Abstract: Neural networks are increasingly used in security applications for intrusion detection on industrial control systems. In this work we examine two areas that must be considered for their effective use. Firstly, is their vulnerability to adversarial attacks when used in a time series setting. Secondly, is potential over-estimation of performance arising from data leakage artefacts. #R##N#To investigate these areas we implement a long short-term memory (LSTM) based intrusion detection system (IDS) which effectively detects cyber-physical attacks on a water treatment testbed representing a strong baseline IDS. #R##N#For investigating adversarial attacks we model two different white box attackers. The first attacker is able to manipulate sensor readings on a subset of the Secure Water Treatment (SWaT) system. By creating a stream of adversarial data the attacker is able to hide the cyber-physical attacks from the IDS. For the cyber-physical attacks which are detected by the IDS, the attacker required on average 2.48 out of 12 total sensors to be compromised for the cyber-physical attacks to be hidden from the IDS. The second attacker model we explore is an $L_{\infty}$ bounded attacker who can send fake readings to the IDS, but to remain imperceptible, limits their perturbations to the smallest $L_{\infty}$ value needed. #R##N#Additionally, we examine data leakage problems arising from tuning for $F_1$ score on the whole SWaT attack set and propose a method to tune detection parameters that does not utilise any attack data. If attack after-effects are accounted for then our new parameter tuning method achieved an $F_1$ score of 0.811$\pm$0.0103.

Includes members:
  Member "deep latent defence":
    also connected through other hyperedges:
      (none)
  Member "efficient cyber attacks detection in industrial control systems using lightweight neural networks":
    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 "real time evasion attacks with physical constraints on deep learning based anomaly detectors in industrial control systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "adversarial patch":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sentinet detecting localized universal attacks against deep learning systems")
  Member "anomaly detection for water treatment system based on neural network with automatic architecture optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "anomaly detection for industrial control systems using sequence to sequence neural networks")
  Member "deep k nearest neighbors towards confident interpretable and robust deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robustness for non parametric classification a generic attack and defense")
  Member "early stage malware prediction using recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neurlux dynamic malware analysis without feature engineering")

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)