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: "time is of the essence machine learning based intrusion detection in industrial time series data"
  Abstract: The Industrial Internet of Things drastically increases connectivity of devices in industrial applications. In addition to the benefits in efficiency, scalability and ease of use, this creates novel attack surfaces. Historically, industrial networks and protocols do not contain means of security, such as authentication and encryption, that are made necessary by this development. Thus, industrial IT-security is needed. In this work, emulated industrial network data is transformed into a time series and analysed with three different algorithms. The data contains labeled attacks, so the performance can be evaluated. Matrix Profiles perform well with almost no parameterisation needed. Seasonal Autoregressive Integrated Moving Average performs well in the presence of noise, requiring parameterisation effort. Long Short Term Memory-based neural networks perform mediocre while requiring a high training- and parameterisation effort.

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"
    - "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"
    - "evaluation of machine learning based anomaly detection algorithms on an industrial modbus tcp data set"
    - "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"
    - "evaluation of machine learning based anomaly detection algorithms on an industrial modbus tcp data set"
    - "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"
    - "evaluation of machine learning based anomaly detection algorithms on an industrial modbus tcp data set"
    - "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"
    - "evaluation of machine learning based anomaly detection algorithms on an industrial modbus tcp data set"
    - "two decades of scada exploitation a brief history"
    - "graph based anomaly detection and description a survey"

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.LG(Machine Learning)