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: "anomaly based intrusion detection in industrial data with svm and random forests"
  Abstract: Attacks on industrial enterprises are increasing in number as well as in effect. Since the introduction of industrial control systems in the 1970's, industrial networks have been the target of malicious actors. More recently, the political and warfare-aspects of attacks on industrial and critical infrastructure are becoming more relevant. In contrast to classic home and office IT systems, industrial IT, so-called OT systems, have an effect on the physical world. Furthermore, industrial devices have long operation times, sometimes several decades. Updates and fixes are tedious and often not possible. The threats on industry with the legacy requirements of industrial environments creates the need for efficient intrusion detection that can be integrated into existing systems. In this work, the network data containing industrial operation is analysed with machine learning- and time series- based anomaly detection algorithms in order to discover the attacks introduced to the data. Two different data sets are used, one Modbus-based gas pipeline control traffic and one OPC UA-based batch processing traffic. In order to detect attacks, two machine learning-based algorithms are used, namely \textit{SVM} and Random Forest. Both perform well, with Random Forest slightly outperforming SVM. Furthermore, extracting and selecting features as well as handling missing data is addressed in this work.

Connected through hyperedge HE-1 (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"
    - "using temporal and topological features for intrusion detection in operational networks"
    - "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)