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: "implementing scada scenarios and introducing attacks to obtain training data for intrusion detection methods"
  Abstract: There are hardly any data sets publicly available that can be used to evaluate intrusion detection algorithms. The biggest threat for industrial applications arises from state-sponsored and criminal groups. Often, formerly unknown exploits are employed by these attackers, so-called 0-day exploits. They cannot be discovered with signature-based intrusion detection. Thus, statistical or machine learning based anomaly detection lends itself readily. These methods especially, however, need a large amount of labelled training data. In this work, an exemplary industrial use case with real-world industrial hardware is presented. Siemens S7 Programmable Logic Controllers are used to control a real world-based control application using the OPC UA protocol: A pump, filling and emptying water tanks. This scenario is used to generate application specific network data. Furthermore, attacks are introduced into this data set. This is done in three ways: First, the normal process is monitored and captured. Common attacks are then synthetically introduced into this data set. Second, malicious behaviour is implemented on the Programmable Logic Controller program and executed live, the traffic is captured as well. Third, malicious behaviour is implemented on the Programmable Logic Controller while still keeping the same output behaviour as in normal operation. An attacker could exploit an application but forge valid sensor output so that no anomaly is detected. Sensors are employed, capturing temperature, sound and flow of water to create data that can be correlated to the network data and used to still detect the attack. All data is labelled, containing the ground truth, meaning all attacks are known and no unknown attacks occur. This makes them perfect for training of anomaly detection algorithms. The data is published to enable security researchers to evaluate intrusion detection solutions.

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"
    - "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"
    - "two decades of scada exploitation a brief history"
    - "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"
    - "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"
    - "two decades of scada exploitation a brief history"

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"
    - "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"
    - "two decades of scada exploitation a brief history"

Connected through hyperedge HE-4 (induced by source paper "devil in the detail attack scenarios in industrial applications"):
  with members:
    - "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"
    - "two decades of scada exploitation a brief history"

Connected through hyperedge HE-5 (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"
    - "two decades of scada exploitation a brief history"

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)