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: "assessing the effectiveness of attack detection at a hackfest on industrial control systems"
  Abstract: A hackfest named SWaT Security Showdown (S3) has been organized consecutively for two years. S3 has enabled researchers and practitioners to assess the effectiveness of methods and products aimed at detecting cyber attacks launched in real-time on an operational water treatment plant, namely, Secure Water Treatment (SWaT). In S3 independent attack teams design and launch attacks on SWaT while defence teams protect the plant passively and raise alarms upon attack detection. Attack teams are scored according to how successful they are in performing attacks based on specific intents while the defense teams are scored based on the effectiveness of their methods to detect the attacks. This paper focuses on the first two instances of S3 and summarizes the benefits of hackfest and the performance of an attack detection mechanism, named Water Defense, that was exposed to attackers during S3.

Connected through hyperedge HE-1 (induced by source paper "learning guided network fuzzing for testing cyber physical system defences"):
  with members:
    - "detecting zero day controller hijacking attacks on the power grid with enhanced deep learning"
    - "learning from mutants using code mutation to learn and monitor invariants of a cyber physical system"
    - "anomaly detection for a water treatment system using unsupervised machine learning"
    - "log based anomaly detection of cps using a statistical method"
    - "a formal approach to cyber physical attacks"
    - "towards concolic testing for hybrid systems"
    - "towards learning and verifying invariants of cyber physical systems by code mutation"
    - "api design for machine learning software experiences from the scikit learn project"

Connected through hyperedge HE-2 (induced by source paper "investigation of cyber attacks on a water distribution system"):
  with members:
    - "learning from mutants using code mutation to learn and monitor invariants of a cyber physical system"
    - "cyber physical systems security a survey"

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)