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: "two decades of scada exploitation a brief history"
  Abstract: Since the early 1960, industrial process control has been applied by electric systems. In the mid 1970's, the term SCADA emerged, describing the automated control and data acquisition. Since most industrial and automation networks were physically isolated, security was not an issue. This changed, when in the early 2000's industrial networks were opened to the public internet. The reasons were manifold. Increased interconnectivity led to more productivity, simplicity and ease of use. It decreased the configuration overhead and downtimes for system adjustments. However, it also led to an abundance of new attack vectors. In recent time, there has been a remarkable amount of attacks on industrial companies and infrastructures. In this paper, known attacks on industrial systems are analysed. This is done by investigating the exploits that are available on public sources. The different types of attacks and their points of entry are reviewed in this paper. Trends in exploitation as well as targeted attack campaigns against industrial enterprises are introduced.

Connected through hyperedge HE-1 (induced by source paper "discussing the feasibility of acoustic sensors for side channel aided industrial intrusion detection an essay"):
  with members:
    - "modern problems require modern solutions hybrid concepts for industrial intrusion detection"
    - "devil in the detail attack scenarios in industrial applications"
    - "implementing scada scenarios and introducing attacks to obtain training data for intrusion detection methods"
    - "putting together the pieces a concept for holistic industrial intrusion detection"
    - "evaluation of machine learning based anomaly detection algorithms on an industrial modbus tcp data set"
    - "time is of the essence machine learning based intrusion detection in industrial time series data"
    - "side channel based intrusion detection for industrial control systems"

Connected through hyperedge HE-2 (induced by source paper "modern problems require modern solutions hybrid concepts for industrial intrusion detection"):
  with members:
    - "devil in the detail attack scenarios in industrial applications"
    - "highly scalable and flexible model for effective aggregation of context based data in generic iiot scenarios"
    - "implementing scada scenarios and introducing attacks to obtain training data for intrusion detection methods"
    - "evaluation of machine learning based anomaly detection algorithms on an industrial modbus tcp data set"
    - "time is of the essence machine learning based intrusion detection in industrial time series data"
    - "a question of context enhancing intrusion detection by providing context information"

Connected through hyperedge HE-3 (induced by source paper "anomaly based intrusion detection in industrial data with svm and random forests"):
  with members:
    - "modern problems require modern solutions hybrid concepts for industrial intrusion detection"
    - "implementing scada scenarios and introducing attacks to obtain training data for intrusion detection methods"
    - "using temporal and topological features for intrusion detection in operational networks"
    - "putting together the pieces a concept for holistic industrial intrusion detection"
    - "evaluation of machine learning based anomaly detection algorithms on an industrial modbus tcp data set"
    - "time is of the essence machine learning based intrusion detection in industrial time series data"

Connected through hyperedge HE-4 (induced by source paper "devil in the detail attack scenarios in industrial applications"):
  with members:
    - "implementing scada scenarios and introducing attacks to obtain training data for intrusion detection methods"
    - "evaluation of machine learning based anomaly detection algorithms on an industrial modbus tcp data set"
    - "time is of the essence machine learning based intrusion detection in industrial time series data"
    - "a question of context enhancing intrusion detection by providing context information"

Connected through hyperedge HE-5 (induced by source paper "security in process detecting attacks in industrial process data"):
  with members:
    - "security in process visually supported triage analysis in industrial process data"
    - "devil in the detail attack scenarios in industrial applications"
    - "anomaly based intrusion detection in industrial data with svm and random forests"
    - "using temporal and topological features for intrusion detection in operational networks"

Connected through hyperedge HE-6 (induced by source paper "using temporal and topological features for intrusion detection in operational networks"):
  with members:
    - "highly scalable and flexible model for effective aggregation of context based data in generic iiot scenarios"
    - "evaluation of machine learning based anomaly detection algorithms on an industrial modbus tcp data set"
    - "time is of the essence machine learning based intrusion detection in industrial time series data"
    - "graph based anomaly detection and description a survey"

Connected through hyperedge HE-7 (induced by source paper "security in process visually supported triage analysis in industrial process data"):
  with members:
    - "devil in the detail attack scenarios in industrial applications"
    - "security in process detecting attacks in industrial process data"
    - "implementing scada scenarios and introducing attacks to obtain training data for intrusion detection methods"

Connected through hyperedge HE-8 (induced by source paper "implementing scada scenarios and introducing attacks to obtain training data for intrusion detection methods"):
  with members:
    - "the dos and don ts of industrial network simulation a field report"

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)