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 "dynamical systems as temporal feature spaces"):
  Abstract: Parameterized state space models in the form of recurrent networks are often used in machine learning to learn from data streams exhibiting temporal dependencies. To break the black box nature of such models it is important to understand the dynamical features of the input driving time series that are formed in the state space. We propose a framework for rigorous analysis of such state representations in vanishing memory state space models such as echo state networks (ESN). In particular, we consider the state space a temporal feature space and the readout mapping from the state space a kernel machine operating in that feature space. We show that: (1) The usual ESN strategy of randomly generating input-to-state, as well as state coupling leads to shallow memory time series representations, corresponding to cross-correlation operator with fast exponentially decaying coefficients; (2) Imposing symmetry on dynamic coupling yields a constrained dynamic kernel matching the input time series with straightforward exponentially decaying motifs or exponentially decaying motifs of the highest frequency; (3) Simple cycle high-dimensional reservoir topology specified only through two free parameters can implement deep memory dynamic kernels with a rich variety of matching motifs. We quantify richness of feature representations imposed by dynamic kernels and demonstrate that for dynamic kernel associated with cycle reservoir topology, the kernel richness undergoes a phase transition close to the edge of stability.

Includes members:
  Member "recent advances in physical reservoir computing a review":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "advectivenet an eulerian lagrangian fluidic reservoir for point cloud processing")
  Member "echo state networks are universal":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optical reservoir computing using multiple light scattering for chaotic systems prediction")
  Member "reservoir computing universality with stochastic inputs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recent advances in physical reservoir computing a review")
  Member "gated orthogonal recurrent units on learning to forget":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generating high fidelity synthetic time series datasets with doppelganger")
  Member "universal discrete time reservoir computers with stochastic inputs and linear readouts using non homogeneous state affine systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "risk bounds for reservoir computing")
  Member "the asymptotic performance of linear echo state neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "risk bounds for reservoir computing")
  Member "nonlinear memory capacity of parallel time delay reservoir computers in the processing of multidimensional signals":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "risk bounds for reservoir computing")
  Member "guided self organization of input driven recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "input anticipating critical reservoirs show power law forgetting of unexpected input events")

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)