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: "filter based regularisation for impulse response modelling"
  Abstract: In the last years, the success of kernel-based regularisation techniques in solving impulse response modelling tasks has revived the interest on linear system identification. In this work, an alternative perspective on the same problem is introduced. Instead of relying on a Bayesian framework to include assumptions about the system in the definition of the covariance matrix of the parameters, here the prior knowledge is injected at the cost function level. The key idea is to define the regularisation matrix as a filtering operation on the parameters, which allows for a more intuitive formulation of the problem from an engineering point of view. Moreover, this results in a unified framework to model low-pass, band-pass and high-pass systems, and systems with one or more resonances. The proposed filter-based approach outperforms the existing regularisation method based on the TC and DC kernels, as illustrated by means of Monte Carlo simulations on several linear modelling examples.

Connected through hyperedge HE-1 (induced by source paper "efficient multidimensional regularization for volterra series estimation"):
  with members:
    - "regularized nonparametric volterra kernel estimation"

Connected through hyperedge HE-2 (induced by source paper "on input design for regularized lti system identification power constrained input"):
  with members:
    - "on asymptotic properties of hyperparameter estimators for kernel based regularization methods"
    - "on kernel design for regularized lti system identification"
    - "maximum entropy properties of discrete time first order stable spline kernel"
    - "regularization and bayesian learning in dynamical systems past present and future"
    - "regularized system identification using orthonormal basis functions"

Connected through hyperedge HE-3 (induced by source paper "on asymptotic properties of hyperparameter estimators for kernel based regularization methods"):
  with members:
    - "on kernel design for regularized lti system identification"
    - "maximum entropy properties of discrete time first order stable spline kernel"
    - "kernels for linear time invariant system identification"

Connected through hyperedge HE-4 (induced by source paper "on kernel design for regularized lti system identification"):
  with members:
    - "on asymptotic properties of hyperparameter estimators for kernel based regularization methods"
    - "continuous time dc kernel a stable generalized first order spline kernel"
    - "on input design for regularized lti system identification power constrained input"
    - "maximum entropy properties of discrete time first order stable spline kernel"
    - "regularization and bayesian learning in dynamical systems past present and future"
    - "kernels for linear time invariant system identification"

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.SY(Systems and Control)