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: "the asymptotic performance of linear echo state neural networks"
  Abstract: In this article, a study of the mean-square error (MSE) performance of linear echo-state neural networks is performed, both for training and testing tasks. Considering the realistic setting of noise present at the network nodes, we derive deterministic equivalents for the aforementioned MSE in the limit where the number of input data $T$ and network size $n$ both grow large. Specializing then the network connectivity matrix to specific random settings, we further obtain simple formulas that provide new insights on the performance of such networks.

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"
    - "nonlinear memory capacity of parallel time delay reservoir computers in the processing of multidimensional signals"

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"
    - "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-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"
    - "nonlinear memory capacity of parallel time delay reservoir computers in the processing of multidimensional signals"
    - "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"
    - "nonlinear memory capacity of parallel time delay reservoir computers in the processing of multidimensional signals"
    - "speech recognition with deep recurrent neural networks"
    - "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.LG(Machine Learning)