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: "semi supervised learning with deep generative models for asset failure prediction"
  Abstract: This work presents a novel semi-supervised learning approach for data-driven modeling of asset failures when health status is only partially known in historical data. We combine a generative model parameterized by deep neural networks with non-linear embedding technique. It allows us to build prognostic models with the limited amount of health status information for the precise prediction of future asset reliability. The proposed method is evaluated on a publicly available dataset for remaining useful life (RUL) estimation, which shows significant improvement even when a fraction of the data with known health status is as sparse as 1% of the total. Our study suggests that the non-linear embedding based on a deep generative model can efficiently regularize a complex model with deep architectures while achieving high prediction accuracy that is far less sensitive to the availability of health status information.

Connected through hyperedge HE-1 (induced by source paper "feature learning for fault detection in high dimensional condition monitoring signals"):
  with members:
    - "multi sensor prognostics using an unsupervised health index based on lstm encoder decoder"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-2 (induced by source paper "a comparative study between bayesian and frequentist neural networks for remaining useful life estimation in condition based maintenance"):
  with members:
    - "a comprehensive guide to bayesian convolutional neural network with variational inference"
    - "sampling free epistemic uncertainty estimation using approximated variance propagation"
    - "temporal convolutional memory networks for remaining useful life estimation of industrial machinery"
    - "a survey on data collection for machine learning a big data ai integration perspective"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "predicting remaining useful life using time series embeddings based on recurrent neural networks"
    - "normalization a preprocessing stage"

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"
    - "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"
    - "recurrent neural network regularization"
    - "dropout improves recurrent neural networks for handwriting recognition"

Connected through hyperedge HE-4 (induced by source paper "knowledge induced learning with adaptive sampling variational autoencoders for open set fault diagnostics"):
  with members:
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "considering race a problem of transfer learning"):
  with members:
    - "age progression regression by conditional adversarial autoencoder"
    - "gp gan towards realistic high resolution image blending"
    - "do they all look the same deciphering chinese japanese and koreans by fine grained deep learning"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-6 (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"
    - "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"
    - "multi sensor prognostics using an unsupervised health index based on lstm encoder decoder"

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)