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: "nonlinear memory capacity of parallel time delay reservoir computers in the processing of multidimensional signals"
  Abstract: This paper addresses the reservoir design problem in the context of delay-based reservoir computers for multidimensional input signals, parallel architectures, and real-time multitasking. First, an approximating reservoir model is presented in those frameworks that provides an explicit functional link between the reservoir parameters and architecture and its performance in the execution of a specific task. Second, the inference properties of the ridge regression estimator in the multivariate context is used to assess the impact of finite sample training on the decrease of the reservoir capacity. Finally, an empirical study is conducted that shows the adequacy of the theoretical results with the empirical performances exhibited by various reservoir architectures in the execution of several nonlinear tasks with multidimensional inputs. Our results confirm the robustness properties of the parallel reservoir architecture with respect to task misspecification and parameter choice that had already been documented in the literature.

Connected through hyperedge HE-1 (induced by source paper "risk bounds for reservoir computing"):
  with members:
    - "differentiable reservoir computing"
    - "echo state networks are universal"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "dynamical systems as temporal feature spaces"):
  with members:
    - "recent advances in physical reservoir computing a review"
    - "echo state networks are universal"
    - "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"
    - "guided self organization of input driven recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "differentiable reservoir computing"):
  with members:
    - "echo state networks are universal"
    - "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"
    - "optoelectronic reservoir computing"

Connected through hyperedge HE-4 (induced by source paper "universal discrete time reservoir computers with stochastic inputs and linear readouts using non homogeneous state affine systems"):
  with members:
    - "reservoir computing universality with stochastic inputs"
    - "the asymptotic performance of linear echo state neural networks"
    - "speech recognition with deep recurrent neural networks"
    - "optoelectronic reservoir computing"

Connected through hyperedge HE-5 (induced by source paper "quantitative evaluation of the performance of discrete time reservoir computers in the forecasting filtering and reconstruction of stochastic stationary signals"):
  with members:
    - "optoelectronic reservoir computing"

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)