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: "evaluation of machine learning based anomaly detection algorithms on an industrial modbus tcp data set"
  Abstract: In the context of the Industrial Internet of Things, communication technology, originally used in home and office environments, is introduced into industrial applications. Commercial off-the-shelf products, as well as unified and well-established communication protocols make this technology easy to integrate and use. Furthermore, productivity is increased in comparison to classic industrial control by making systems easier to manage, set up and configure. Unfortunately, most attack surfaces of home and office environments are introduced into industrial applications as well, which usually have very few security mechanisms in place. Over the last years, several technologies tackling that issue have been researched. In this work, machine learning-based anomaly detection algorithms are employed to find malicious traffic in a synthetically generated data set of Modbus/TCP communication of a fictitious industrial scenario. The applied algorithms are Support Vector Machine (SVM), Random Forest, k-nearest neighbour and k-means clustering. Due to the synthetic data set, supervised learning is possible. Support Vector Machine and k-nearest neighbour perform well with different data sets, while k-nearest neighbour and k-means clustering do not perform satisfactorily.

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"
    - "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"
    - "implementing scada scenarios and introducing attacks to obtain training data for intrusion detection methods"
    - "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"
    - "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"
    - "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:
    - "implementing scada scenarios and introducing attacks to obtain training data for intrusion detection methods"
    - "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 "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"
    - "time is of the essence machine learning based intrusion detection in industrial time series data"
    - "two decades of scada exploitation a brief history"
    - "graph based anomaly detection and description a survey"

Connected through hyperedge HE-6 (induced by source paper "the dos and don ts of industrial network simulation a field report"):
  with members:
    - "highly scalable and flexible model for effective aggregation of context based data in generic iiot scenarios"
    - "towards a flexible architecture for industrial networking"
    - "a question of context enhancing intrusion detection by providing context information"
    - "two decades of scada exploitation a brief history"
    - "nosebreak attacking honeynets"

Connected through hyperedge HE-7 (induced by source paper "time is of the essence machine learning based intrusion detection in industrial time series data"):
  with members:
    - "highly scalable and flexible model for effective aggregation of context based data in generic iiot scenarios"
    - "a question of context enhancing intrusion detection by providing context information"
    - "two decades of scada exploitation a brief history"
    - "multivariate industrial time series with cyber attack simulation fault detection using an lstm based predictive data model"

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)