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: "multivariate industrial time series with cyber attack simulation fault detection using an lstm based predictive data model"
  Abstract: We adopted an approach based on an LSTM neural network to monitor and detect faults in industrial multivariate time series data. To validate the approach we created a Modelica model of part of a real gasoil plant. By introducing hacks into the logic of the Modelica model, we were able to generate both the roots and causes of fault behavior in the plant. Having a self-consistent data set with labeled faults, we used an LSTM architecture with a forecasting error threshold to obtain precision and recall quality metrics. The dependency of the quality metric on the threshold level is considered. An appropriate mechanism such as "one handle" was introduced for filtering faults that are outside of the plant operator field of interest.

Connected through hyperedge HE-1 (induced by source paper "anomaly detection in beehives using deep recurrent autoencoders"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "multi sensor prognostics using an unsupervised health index based on lstm encoder decoder"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "time series anomaly detection using convolutional neural networks and transfer learning"):
  with members:
    - "automatic salt deposits segmentation a deep learning approach"
    - "exploring the limits of weakly supervised pretraining"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "automatic brain tumor detection and segmentation using u net based fully convolutional networks"
    - "data augmentation of wearable sensor data for parkinson s disease monitoring using convolutional neural networks"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "multi scale convolutional neural networks for time series classification"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-3 (induced by source paper "on the veracity of cyber intrusion alerts synthesized by generative adversarial networks"):
  with members:
    - "tiresias predicting security events through deep learning"
    - "a style based generator architecture for generative adversarial networks"
    - "idsgan generative adversarial networks for attack generation against intrusion detection"
    - "voice impersonation using generative adversarial networks"
    - "mine mutual information neural estimation"
    - "black box attacks against rnn based malware detection algorithms"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "two birds with one network unifying failure event prediction and time to failure modeling"):
  with members:
    - "machine learning for survival analysis a survey"
    - "semi supervised learning with deep generative models for asset failure prediction"
    - "predicting remaining useful life using time series embeddings based on recurrent neural networks"
    - "multi sensor prognostics using an unsupervised health index based on lstm encoder decoder"

Connected through hyperedge HE-5 (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"
    - "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-6 (induced by source paper "predicting remaining useful life using time series embeddings based on recurrent neural networks"):
  with members:
    - "early detection of combustion instabilities using deep convolutional selective autoencoders on hi speed flame video"
    - "lstm based encoder decoder for multi sensor anomaly detection"
    - "multi sensor prognostics using an unsupervised health index based on lstm encoder decoder"
    - "deep learning and its applications to machine health monitoring a survey"
    - "recurrent neural networks for multivariate time series with missing values"
    - "unsupervised learning of video representations using lstms"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "semi supervised sequence learning"

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)