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: "devil in the detail attack scenarios in industrial applications"
  Abstract: In the past years, industrial networks have become increasingly interconnected and opened to private or public networks. This leads to an increase in efficiency and manageability, but also increases the attack surface. Industrial networks often consist of legacy systems that have not been designed with security in mind. In the last decade, an increase in attacks on cyber-physical systems was observed, with drastic consequences on the physical work. In this work, attack vectors on industrial networks are categorised. A real-world process is simulated, attacks are then introduced. Finally, two machine learning-based methods for time series anomaly detection are employed to detect the attacks. Matrix Profiles are employed more successfully than a predictor Long Short-Term Memory network, a class of neural networks.

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

Connected through hyperedge HE-3 (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"
    - "anomaly based intrusion detection in industrial data with svm and random forests"
    - "using temporal and topological features for intrusion detection in operational networks"
    - "two decades of scada exploitation a brief history"

Connected through hyperedge HE-4 (induced by source paper "security in process visually supported triage analysis in industrial process data"):
  with members:
    - "security in process detecting attacks in industrial process data"
    - "implementing scada scenarios and introducing attacks to obtain training data for intrusion detection methods"
    - "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)