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: "multi sensor prognostics using an unsupervised health index based on lstm encoder decoder"
  Abstract: Many approaches for estimation of Remaining Useful Life (RUL) of a machine, using its operational sensor data, make assumptions about how a system degrades or a fault evolves, e.g., exponential degradation. However, in many domains degradation may not follow a pattern. We propose a Long Short Term Memory based Encoder-Decoder (LSTM-ED) scheme to obtain an unsupervised health index (HI) for a system using multi-sensor time-series data. LSTM-ED is trained to reconstruct the time-series corresponding to healthy state of a system. The reconstruction error is used to compute HI which is then used for RUL estimation. We evaluate our approach on publicly available Turbofan Engine and Milling Machine datasets. We also present results on a real-world industry dataset from a pulverizer mill where we find significant correlation between LSTM-ED based HI and maintenance costs.

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"
    - "multivariate industrial time series with cyber attack simulation fault detection using an lstm based predictive data model"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "feature learning for fault detection in high dimensional condition monitoring signals"):
  with members:
    - "semi supervised learning with deep generative models for asset failure prediction"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "data driven prognostics with predictive uncertainty estimation using ensemble of deep ordinal regression models"):
  with members:
    - "deep learning for patient specific kidney graft survival analysis"
    - "semi supervised learning with deep generative models for asset failure prediction"
    - "predicting remaining useful life using time series embeddings based on recurrent neural networks"
    - "recurrent neural network regularization"
    - "dropout improves recurrent neural networks for handwriting recognition"

Connected through hyperedge HE-4 (induced by source paper "a neural network evolutionary computational framework for remaining useful life estimation of mechanical systems"):
  with members:
    - "take it in your stride do we need striding in cnns"

Connected through hyperedge HE-5 (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"
    - "multivariate industrial time series with cyber attack simulation fault detection using an lstm based predictive data model"

Connected through hyperedge HE-6 (induced by source paper "prognostics estimations with dynamic states"):
  with members:
    - "predicting remaining useful life using time series embeddings based on recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "timenet pre trained deep recurrent neural network for time series classification"):
  with members:
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
    - "wavenet a generative model for raw audio"
    - "lstm based encoder decoder for multi sensor anomaly detection"
    - "skip thought vectors"
    - "semi supervised sequence learning"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-8 (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"
    - "multivariate industrial time series with cyber attack simulation fault detection using an lstm based predictive data model"
    - "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)