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: "online tool condition monitoring based on parsimonious ensemble"
  Abstract: Existing methodologies for tool condition monitoring (TCM) still rely on batch approaches which cannot cope with a fast sampling rate of a metal cutting process. Furthermore, they require a retraining process to be completed from scratch when dealing with a new set of machining parameters. This paper presents an online TCM approach based on Parsimonious Ensemble+ (pENsemble+). The unique feature of pENsemble+ lies in its highly flexible principle where both the ensemble structure and base-classifier structure can automatically grow and shrink on the fly based on the characteristics of data streams. Moreover, the online feature selection scenario is integrated to actively sample relevant input attributes. This paper presents advancement of a newly developed ensemble learning algorithm, pENsemble, where the online active learning scenario is incorporated to reduce the operator’s labeling effort. The ensemble merging scenario is proposed which allows reduction of ensemble complexity while retaining its diversity. Experimental studies utilizing two real-world manufacturing data streams: 1) metal turning and 2) 3-D-printing processes and comparisons with well-known algorithms were carried out. Furthermore, the efficacy of pENsemble+ was examined using benchmark concept drift data streams. It has been found that pENsemble+ incurs low structural complexity and results in a significant reduction of the operator’s labeling effort.

Connected through hyperedge HE-1 (induced by source paper "devdan deep evolving denoising autoencoder"):
  with members:
    - "an online rfid localization in the manufacturing shopfloor"
    - "autonomous deep learning continual learning approach for dynamic environments"
    - "evolving ensemble fuzzy classifier"
    - "deep learning for iot big data and streaming analytics a survey"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "stochastic configuration networks fundamentals and algorithms"
    - "online deep learning learning deep neural networks on the fly"

Connected through hyperedge HE-2 (induced by source paper "an incremental construction of deep neuro fuzzy system for continual learning of non stationary data streams"):
  with members:
    - "deep stacked stochastic configuration networks for non stationary data streams"
    - "parsimonious random vector functional link network for data streams"
    - "evolving ensemble fuzzy classifier"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "deep learning for iot big data and streaming analytics a survey"
    - "lifelong learning with dynamically expandable networks"
    - "online deep learning learning deep neural networks on the fly"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-3 (induced by source paper "autonomous deep learning continual learning approach for dynamic environments"):
  with members:
    - "an online rfid localization in the manufacturing shopfloor"
    - "autonomous deep learning incremental learning of denoising autoencoder for evolving data streams"
    - "an incremental construction of deep neuro fuzzy system for continual learning of non stationary data streams"
    - "evolving ensemble fuzzy classifier"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "online deep learning learning deep neural networks on the fly"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-4 (induced by source paper "autonomous deep learning incremental learning of denoising autoencoder for evolving data streams"):
  with members:
    - "deep learning for iot big data and streaming analytics a survey"
    - "lifelong learning with dynamically expandable networks"
    - "online deep learning learning deep neural networks on the fly"
    - "learning the number of neurons in deep networks"
    - "predicting parameters in deep learning"
    - "representation learning a review and new perspectives"

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)