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: "on kernel design for regularized lti system identification"
  Abstract: There are two key issues for the kernel-based regularization method: one is how to design a suitable kernel to embed in the kernel the prior knowledge of the LTI system to be identified, and the other one is how to tune the kernel such that the resulting regularized impulse response estimator can achieve a good bias-variance tradeoff. In this paper, we focus on the issue of kernel design. Depending on the type of the prior knowledge, we propose two methods to design kernels: one is from a machine learning perspective and the other one is from a system theory perspective. We also provide analysis results for both methods, which not only enhances our understanding for the existing kernels but also directs the design of new kernels.

Connected through hyperedge HE-1 (induced by source paper "kernel absolute summability is only sufficient for rkhs stability"):
  with members:
    - "kernels for linear time invariant system identification"

Connected through hyperedge HE-2 (induced by source paper "on input design for regularized lti system identification power constrained input"):
  with members:
    - "filter based regularisation for impulse response modelling"
    - "on asymptotic properties of hyperparameter estimators for kernel based regularization methods"
    - "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:
    - "filter based regularisation for impulse response modelling"
    - "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 "continuous time dc kernel a stable generalized first order spline kernel"):
  with members:
    - "on asymptotic properties of hyperparameter estimators for kernel based regularization methods"
    - "on input design for regularized lti system identification power constrained input"
    - "maximum entropy properties of discrete time first order stable spline kernel"

Connected through hyperedge HE-5 (induced by source paper "regularized nonparametric volterra kernel estimation"):
  with members:
    - "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)