human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deep randomized neural networks"):
  Abstract: Randomized Neural Networks explore the behavior of neural systems where the majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical examples of such systems consist of multi-layered neural network architectures where the connections to the hidden layer(s) are left untrained after initialization. Limiting the training algorithms to operate on a reduced set of weights inherently characterizes the class of Randomized Neural Networks with a number of intriguing features. Among them, the extreme efficiency of the resulting learning processes is undoubtedly a striking advantage with respect to fully trained architectures. Besides, despite the involved simplifications, randomized neural systems possess remarkable properties both in practice, achieving state-of-the-art results in multiple domains, and theoretically, allowing to analyze intrinsic properties of neural architectures (e.g. before training of the hidden layers' connections). In recent years, the study of Randomized Neural Networks has been extended towards deep architectures, opening new research directions to the design of effective yet extremely efficient deep learning models in vectorial as well as in more complex data domains. This chapter surveys all the major aspects regarding the design and analysis of Randomized Neural Networks, and some of the key results with respect to their approximation capabilities. In particular, we first introduce the fundamentals of randomized neural models in the context of feed-forward networks (i.e., Random Vector Functional Link and equivalent models) and convolutional filters, before moving to the case of recurrent systems (i.e., Reservoir Computing networks). For both, we focus specifically on recent results in the domain of deep randomized systems, and (for recurrent models) their application to structured domains.

Includes members:
  Member "richness of deep echo state network dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "echo state neural machine translation")
  Member "what s hidden in a randomly weighted neural network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "good subnetworks provably exist pruning via greedy forward selection")
  Member "intriguing properties of randomly weighted networks generalizing while learning next to nothing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "training batchnorm and only batchnorm on the expressive power of random features in cnns")
  Member "comparison between deepesns and gated rnns on multivariate time series prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "echo state networks with self normalizing activations on the hyper sphere")
  Member "deep semi random features for nonlinear function approximation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressive hyperspherical energy minimization")
  Member "stochastic configuration networks fundamentals and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "error feedback stochastic configuration strategy on convolutional neural networks for time series forecasting")
  Member "multiplex visibility graphs to investigate recurrent neural network dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dynamical anatomy of narma10 benchmark task")
  Member "deep stochastic configuration networks with universal approximation property":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "error feedback stochastic configuration strategy on convolutional neural networks for time series forecasting")

Question: Please tell me which class the source paper 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)