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: "predicting remaining useful life using time series embeddings based on recurrent neural networks"
  Abstract: We consider the problem of estimating the remaining useful life (RUL) of a system or a machine from sensor data. Many approaches for RUL estimation based on sensor data make assumptions about how machines degrade. Additionally, sensor data from machines is noisy and often suffers from missing values in many practical settings. We propose Embed-RUL: a novel approach for RUL estimation from sensor data that does not rely on any degradation-trend assumptions, is robust to noise, and handles missing values. Embed-RUL utilizes a sequence-to-sequence model based on Recurrent Neural Networks (RNNs) to generate embeddings for multivariate time series subsequences. The embeddings for normal and degraded machines tend to be different, and are therefore found to be useful for RUL estimation. We show that the embeddings capture the overall pattern in the time series while filtering out the noise, so that the embeddings of two machines with similar operational behavior are close to each other, even when their sensor readings have significant and varying levels of noise content. We perform experiments on publicly available turbofan engine dataset and a proprietary real-world dataset, and demonstrate that Embed-RUL outperforms the previously reported state-of-the-art on several metrics.

Connected through hyperedge HE-1 (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"
    - "semi supervised learning with deep generative models for asset failure prediction"
    - "normalization a preprocessing stage"

Connected through hyperedge HE-2 (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"
    - "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-3 (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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "mechanisms for integrated feature normalization and remaining useful life estimation using lstms applied to hard disks"):
  with members:
    - "qdds a novel quantum swarm algorithm inspired by a double dirac delta potential"
    - "particle swarm optimization a survey of historical and recent developments with hybridization perspectives"
    - "data clustering using a hybrid of fuzzy c means and quantum behaved particle swarm optimization"
    - "lstm based encoder decoder for multi sensor anomaly detection"

Connected through hyperedge HE-5 (induced by source paper "prognostics estimations with dynamic states"):
  with members:
    - "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)