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: "universal discrete time reservoir computers with stochastic inputs and linear readouts using non homogeneous state affine systems"
  Abstract: A new class of non-homogeneous state-affine systems is introduced. Sufficient conditions are identified that guarantee first, that the associated reservoir computers with linear readouts are causal, time-invariant, and satisfy the fading memory property and second, that a subset of this class is universal in the category of fading memory filters with stochastic almost surely bounded inputs. This means that any discrete-time filter that satisfies the fading memory property with random inputs of that type can be uniformly approximated by elements in the non-homogeneous state-affine family.

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"
    - "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-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"
    - "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-3 (induced by source paper "differentiable reservoir computing"):
  with members:
    - "echo state networks are universal"
    - "reservoir computing universality with stochastic inputs"
    - "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-4 (induced by source paper "adaptive anomaly detection in chaotic time series with a spatially aware echo state network"):
  with members:
    - "fusion of parallel array operations"

Connected through hyperedge HE-5 (induced by source paper "echo state networks are universal"):
  with members:
    - "reservoir computing universality with stochastic inputs"

Connected through hyperedge HE-6 (induced by source paper "reservoir computing universality with stochastic inputs"):
  with members:
    - "echo state networks are universal"

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)