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: "deep learning and its applications to machine health monitoring a survey"
  Abstract: Since 2006, deep learning (DL) has become a rapidly growing research direction, redefining state-of-the-art performances in a wide range of areas such as object recognition, image segmentation, speech recognition and machine translation. In modern manufacturing systems, data-driven machine health monitoring is gaining in popularity due to the widespread deployment of low-cost sensors and their connection to the Internet. Meanwhile, deep learning provides useful tools for processing and analyzing these big machinery data. The main purpose of this paper is to review and summarize the emerging research work of deep learning on machine health monitoring. After the brief introduction of deep learning techniques, the applications of deep learning in machine health monitoring systems are reviewed mainly from the following aspects: Auto-encoder (AE) and its variants, Restricted Boltzmann Machines and its variants including Deep Belief Network (DBN) and Deep Boltzmann Machines (DBM), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Finally, some new trends of DL-based machine health monitoring methods are discussed.

Connected through hyperedge HE-1 (induced by source paper "collaborative inference for efficient remote monitoring"):
  with members:
    - "deep learning for time series classification a review"
    - "efficient inference on deep neural networks by dynamic representations and decision gates"
    - "deep feature compression for collaborative object detection"
    - "doubly nested network for resource efficient inference"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "xgboost a scalable tree boosting system"
    - "doubly convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "structural health monitoring of cantilever beam a case study using bayesian neural network and deep learning"):
  with members:
    - "an introduction to convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-3 (induced by source paper "temporal convolutional memory networks for remaining useful life estimation of industrial machinery"):
  with members:
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "lstm a search space odyssey"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-4 (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"
    - "multi sensor prognostics using an unsupervised health index based on lstm encoder decoder"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "semi supervised learning with deep generative models for asset failure prediction"):
  with members:
    - "multi sensor prognostics using an unsupervised health index based on lstm encoder decoder"
    - "semi supervised learning with ladder networks"
    - "semi supervised learning with deep generative models"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)