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: "stochastic configuration networks fundamentals and algorithms"
  Abstract: This paper contributes to the development of randomized methods for neural networks. The proposed learner model is generated incrementally by stochastic configuration (SC) algorithms, termed SC networks (SCNs). In contrast to the existing randomized learning algorithms for single layer feed-forward networks, we randomly assign the input weights and biases of the hidden nodes in the light of a supervisory mechanism, and the output weights are analytically evaluated in either a constructive or selective manner. As fundamentals of SCN-based data modeling techniques, we establish some theoretical results on the universal approximation property. Three versions of SC algorithms are presented for data regression and classification problems in this paper. Simulation results concerning both data regression and classification indicate some remarkable merits of our proposed SCNs in terms of less human intervention on the network size setting, the scope adaptation of random parameters, fast learning, and sound generalization.

Connected through hyperedge HE-1 (induced by source paper "deep randomized neural networks"):
  with members:
    - "richness of deep echo state network dynamics"
    - "what s hidden in a randomly weighted neural network"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "comparison between deepesns and gated rnns on multivariate time series prediction"
    - "deep semi random features for nonlinear function approximation"
    - "multiplex visibility graphs to investigate recurrent neural network dynamics"
    - "deep stochastic configuration networks with universal approximation property"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"

Connected through hyperedge HE-2 (induced by source paper "error feedback stochastic configuration strategy on convolutional neural networks for time series forecasting"):
  with members:
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "accelerating neural architecture search using performance prediction"
    - "deep stochastic configuration networks with universal approximation property"
    - "temporal attention augmented bilinear network for financial time series data analysis"
    - "a powerful generative model using random weights for the deep image representation"
    - "pso mismo modeling strategy for multistep ahead time series prediction"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "fast construction of correcting ensembles for legacy artificial intelligence systems algorithms and a case study"):
  with members:
    - "correction of ai systems by linear discriminants probabilistic foundations"
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "stochastic separation theorems"
    - "stochastic configuration networks ensemble with heterogeneous features for large scale data analytics"
    - "neurogenesis deep learning extending deep networks to accommodate new classes"
    - "deep stochastic configuration networks with universal approximation property"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "using deep convolutional networks for gesture recognition in american sign language"

Connected through hyperedge HE-4 (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"
    - "evolving ensemble fuzzy classifier"
    - "deep learning for iot big data and streaming analytics a survey"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "online deep learning learning deep neural networks on the fly"

Connected through hyperedge HE-5 (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"
    - "evolving ensemble fuzzy classifier"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "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-6 (induced by source paper "blessing of dimensionality at the edge"):
  with members:
    - "the unreasonable effectiveness of small neural ensembles in high dimensional brain"
    - "fast construction of correcting ensembles for legacy artificial intelligence systems algorithms and a case study"
    - "estimating the effective dimension of large biological datasets using fisher separability analysis"
    - "correction of ai systems by linear discriminants probabilistic foundations"
    - "augmented artificial intelligence"
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "stochastic separation theorems"
    - "game theoretic modeling of driver and vehicle interactions for verification and validation of autonomous vehicle control systems"

Connected through hyperedge HE-7 (induced by source paper "generating random parameters in feedforward neural networks with random hidden nodes drawbacks of the standard method and how to improve it"):
  with members:
    - "improving randomized learning of feedforward neural networks by appropriate generation of random parameters"

Connected through hyperedge HE-8 (induced by source paper "a constructive approach for data driven randomized learning of feedforward neural networks"):
  with members:
    - "data driven randomized learning of feedforward 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.NE(Neural and Evolutionary Computing)