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 stochastic configuration networks with universal approximation property"
  Abstract: This paper develops a randomized approach for incrementally building deep neural networks, where a supervisory mechanism is proposed to constrain the random assignment of the weights and biases, and all the hidden layers have direct links to the output layer. A fundamental result on the universal approximation property is established for such a class of randomized leaner models, namely deep stochastic configuration networks (DeepSCNs). A learning algorithm is presented to implement DeepSCNs with either specific architecture or self-organization. The read-out weights attached with all direct links from each hidden layer to the output layer are evaluated by the least squares method. Given a set of training examples, DeepSCNs can speedily produce a learning representation, that is, a collection of random basis functions with the cascaded inputs together with the read-out weights. An empirical study on a function approximation is carried out to demonstrate some properties of the proposed deep learner model.

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"
    - "stochastic configuration networks fundamentals and algorithms"
    - "multiplex visibility graphs to investigate recurrent neural network dynamics"
    - "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"
    - "stochastic configuration networks fundamentals and algorithms"
    - "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 fundamentals and algorithms"
    - "stochastic configuration networks ensemble with heterogeneous features for large scale data analytics"
    - "neurogenesis deep learning extending deep networks to accommodate new classes"
    - "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 "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"
    - "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 networks with stochastic depth"

Connected through hyperedge HE-5 (induced by source paper "two dimensional stochastic configuration networks for image data analytics"):
  with members:
    - "learning less overlapping representations"
    - "recovery guarantees for one hidden layer neural networks"
    - "stochastic configuration networks ensemble with heterogeneous features for large scale data analytics"
    - "matrix neural networks"
    - "approximation with random bases"
    - "matrix variate rbm model with gaussian distributions"
    - "on the generalization error bounds of neural networks under diversity inducing mutual angular regularization"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-6 (induced by source paper "stochastic configuration networks fundamentals and algorithms"):
  with members:
    - "stochastic configuration networks ensemble with heterogeneous features for large scale data analytics"
    - "approximation with random bases"
    - "speech recognition with deep recurrent neural networks"
    - "randomized algorithms for matrices and data"
    - "feasibility of random basis function approximators for modeling and control"

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)