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: "echo state networks are universal"
  Abstract: This paper shows that echo state networks are universal uniform approximants in the context of discrete-time fading memory filters with uniformly bounded inputs defined on negative infinite times. This result guarantees that any fading memory input/output system in discrete time can be realized as a simple finite-dimensional neural network-type state-space model with a static linear readout map. This approximation is valid for infinite time intervals. The proof of this statement is based on fundamental results, also presented in this work, about the topological nature of the fading memory property and about reservoir computing systems generated by continuous reservoir maps.

Connected through hyperedge HE-1 (induced by source paper "optical reservoir computing using multiple light scattering for chaotic systems prediction"):
  with members:
    - "recent advances in physical reservoir computing a review"
    - "a substrate independent framework to characterise reservoir computers"
    - "scaling up echo state networks with multiple light scattering"
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "newma a new method for scalable model free online change point detection"
    - "using a reservoir computer to learn chaotic attractors with applications to chaos synchronization and cryptography"
    - "hardware for machine learning challenges and opportunities"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-2 (induced by source paper "recent advances in physical reservoir computing a review"):
  with members:
    - "distributive dynamic spectrum access through deep reinforcement learning a reservoir computing based approach"
    - "reservoir computing hardware with cellular automata"
    - "scaling up echo state networks with multiple light scattering"
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "reservoir computing universality with stochastic inputs"
    - "the case for rejecting the memristor as a fundamental circuit element"
    - "reservoir computing using non uniform binary cellular automata"
    - "computing with networks of nonlinear mechanical oscillators"

Connected through hyperedge HE-3 (induced by source paper "risk bounds for reservoir computing"):
  with members:
    - "differentiable reservoir computing"
    - "reservoir computing universality with stochastic inputs"
    - "theory and algorithms for forecasting time series"
    - "spectrally normalized margin bounds for neural networks"
    - "universal discrete time reservoir computers with stochastic inputs and linear readouts using non homogeneous state affine systems"
    - "difference between memory and prediction in linear recurrent networks"
    - "the asymptotic performance of linear echo state neural networks"
    - "nonlinear memory capacity of parallel time delay reservoir computers in the processing of multidimensional signals"

Connected through hyperedge HE-4 (induced by source paper "echo state networks with self normalizing activations on the hyper sphere"):
  with members:
    - "interpreting recurrent neural networks behaviour via excitable network attractors"
    - "reservoir computing approaches for representation and classification of multivariate time series"
    - "chasing the echo state property"
    - "a characterization of the edge of criticality in binary echo state networks"
    - "hybrid forecasting of chaotic processes using machine learning in conjunction with a knowledge based model"
    - "comparison between deepesns and gated rnns on multivariate time series prediction"
    - "reservoir computing on the hypersphere"
    - "difference between memory and prediction in linear recurrent networks"

Connected through hyperedge HE-5 (induced by source paper "data driven reconstruction of nonlinear dynamics from sparse observation"):
  with members:
    - "short note on the behavior of recurrent neural network for noisy dynamical system"
    - "brits bidirectional recurrent imputation for time series"
    - "using a reservoir computer to learn chaotic attractors with applications to chaos synchronization and cryptography"
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"
    - "recurrent neural networks for multivariate time series with missing values"
    - "deep learning in neural networks"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-6 (induced by source paper "dynamical systems as temporal feature spaces"):
  with members:
    - "recent advances in physical reservoir computing a review"
    - "reservoir computing universality with stochastic inputs"
    - "gated orthogonal recurrent units on learning to forget"
    - "universal discrete time reservoir computers with stochastic inputs and linear readouts using non homogeneous state affine systems"
    - "the asymptotic performance of linear echo state neural networks"
    - "nonlinear memory capacity of parallel time delay reservoir computers in the processing of multidimensional signals"
    - "guided self organization of input driven recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "differentiable reservoir computing"):
  with members:
    - "reservoir computing universality with stochastic inputs"
    - "universal discrete time reservoir computers with stochastic inputs and linear readouts using non homogeneous state affine systems"
    - "the asymptotic performance of linear echo state neural networks"
    - "nonlinear memory capacity of parallel time delay reservoir computers in the processing of multidimensional signals"
    - "optoelectronic reservoir computing"

Connected through hyperedge HE-8 (induced by source paper "reservoir topology in deep echo state networks"):
  with members:
    - "short term memory of deep rnn"
    - "concentric esn assessing the effect of modularity in cycle reservoirs"
    - "deep echo state network deepesn a brief survey"
    - "how to construct deep recurrent 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)