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 ensemble with heterogeneous features for large scale data analytics"
  Abstract: Abstract   This paper presents a fast decorrelated neuro-ensemble with heterogeneous features for large-scale data analytics, where stochastic configuration networks (SCNs) are employed as base learner models and the well-known negative correlation learning (NCL) strategy is adopted to evaluate the output weights. By feeding a large number of samples into the SCN base models, we obtain a huge sized linear equation system which is difficult to be solved by means of computing a pseudo-inverse used in the least squares method. Based on the group of heterogeneous features, the block Jacobi and Gauss–Seidel methods are employed to iteratively evaluate the output weights, and a convergence analysis is given with a demonstration on the uniqueness of these iterative solutions. Experiments with comparisons on two large-scale datasets are carried out, and the system robustness with respect to the regularizing factor used in NCL is given. Results indicate that the proposed ensemble learning techniques have good potential for resolving large-scale data modelling problems.

Connected through hyperedge HE-1 (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"
    - "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-2 (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"
    - "deep stochastic configuration networks with universal approximation property"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-3 (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"
    - "deep stochastic configuration networks with universal approximation property"
    - "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-4 (induced by source paper "parsimonious random vector functional link network for data streams"):
  with members:
    - "stochastic configuration networks fundamentals and algorithms"
    - "feasibility of random basis function approximators for modeling and control"

Connected through hyperedge HE-5 (induced by source paper "stochastic configuration networks fundamentals and algorithms"):
  with members:
    - "deep stochastic configuration networks with universal approximation property"
    - "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)