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: "evolving ensemble fuzzy classifier"
  Abstract: The concept of ensemble learning offers a promising avenue in learning from data streams under complex environments because it better addresses the bias and variance dilemma than its single-model counterpart and features a reconfigurable structure, which is well suited to the given context. While various extensions of ensemble learning for mining nonstationary data streams can be found in the literature, most of them are crafted under static base-classifier and revisit preceding samples in the sliding window for a retraining step. This feature causes computationally prohibitive complexity and is not flexible enough to cope with rapidly changing environments. Their complexities are often demanding because they involve a large collection of offline classifiers due to the absence of structural complexities reduction mechanisms and lack of an online feature selection mechanism. A novel evolving ensemble classifier, namely Parsimonious Ensemble (pENsemble), is proposed in this paper. pENsemble differs from existing architectures in the fact that it is built upon an evolving classifier from data streams, termed Parsimonious Classifier. pENsemble is equipped by an ensemble pruning mechanism, which estimates a localized generalization error of a base classifier. A dynamic online feature selection scenario is integrated into the pENsemble. This method allows for dynamic selection and deselection of input features on the fly. pENsemble adopts a dynamic ensemble structure to output a final classification decision where it features a novel drift detection scenario to grow the ensemble's structure. The efficacy of the pENsemble has been numerically demonstrated through rigorous numerical studies with dynamic and evolving data streams, where it delivers the most encouraging performance in attaining a tradeoff between accuracy and complexity.

Connected through hyperedge HE-1 (induced by source paper "online tool condition monitoring based on parsimonious ensemble"):
  with members:
    - "metacognitive learning approach for online tool condition monitoring"

Connected through hyperedge HE-2 (induced by source paper "devdan deep evolving denoising autoencoder"):
  with members:
    - "online tool condition monitoring based on parsimonious ensemble"
    - "an online rfid localization in the manufacturing shopfloor"
    - "autonomous deep learning continual learning approach for dynamic environments"
    - "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-3 (induced by source paper "deep stacked stochastic configuration networks for lifelong learning of non stationary data streams"):
  with members:
    - "autonomous deep learning incremental learning of denoising autoencoder for evolving data streams"
    - "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"
    - "stochastic configuration networks ensemble with heterogeneous features for large scale data analytics"
    - "deep stochastic configuration networks with universal approximation property"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-4 (induced by source paper "automatic construction of multi layer perceptron network from streaming examples"):
  with members:
    - "autonomous deep learning continual learning approach for dynamic environments"
    - "an empirical study of example forgetting during deep neural network learning"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "online deep learning learning deep neural networks on the fly"
    - "gradient episodic memory for continual learning"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-5 (induced by source paper "an incremental construction of deep neuro fuzzy system for continual learning of non stationary data streams"):
  with members:
    - "online tool condition monitoring based on parsimonious ensemble"
    - "deep stacked stochastic configuration networks for non stationary data streams"
    - "parsimonious random vector functional link network for data streams"
    - "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-6 (induced by source paper "autonomous deep learning continual learning approach for dynamic environments"):
  with members:
    - "online tool condition monitoring based on parsimonious ensemble"
    - "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"
    - "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"

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)