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 "learning guided network fuzzing for testing cyber physical system defences"):
  Abstract: The threat of attack faced by cyber-physical systems (CPSs), especially when they play a critical role in automating public infrastructure, has motivated research into a wide variety of attack defence mechanisms. Assessing their effectiveness is challenging, however, as realistic sets of attacks to test them against are not always available. In this paper, we propose smart fuzzing, an automated, machine learning guided technique for systematically finding 'test suites' of CPS network attacks, without requiring any knowledge of the system's control programs or physical processes. Our approach uses predictive machine learning models and metaheuristic search algorithms to guide the fuzzing of actuators so as to drive the CPS into different unsafe physical states. We demonstrate the efficacy of smart fuzzing by implementing it for two real-world CPS testbeds—a water purification plant and a water distribution system—finding attacks that drive them into 27 different unsafe states involving water flow, pressure, and tank levels, including six that were not covered by an established attack benchmark. Finally, we use our approach to test the effectiveness of an invariant-based defence system for the water treatment plant, finding two attacks that were not detected by its physical invariant checks, highlighting a potential weakness that could be exploited in certain conditions.

Includes members:
  Member "detecting zero day controller hijacking attacks on the power grid with enhanced deep learning":
    also connected through other hyperedges:
      (none)
  Member "assessing the effectiveness of attack detection at a hackfest on industrial control systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "investigation of cyber attacks on a water distribution system")
  Member "learning from mutants using code mutation to learn and monitor invariants of a cyber physical system":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "conaml constrained adversarial machine learning for cyber physical systems")
  Member "log based anomaly detection of cps using a statistical method":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning from mutants using code mutation to learn and monitor invariants of a cyber physical system")
  Member "a formal approach to cyber physical attacks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "state of the art of cyber physical systems security an automatic control perspective")
  Member "towards concolic testing for hybrid systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning from mutants using code mutation to learn and monitor invariants of a cyber physical system")
  Member "towards learning and verifying invariants of cyber physical systems by code mutation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning from mutants using code mutation to learn and monitor invariants of a cyber physical system")

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)