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 "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.

Includes members:
  Member "filter based regularisation for impulse response modelling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient multidimensional regularization for volterra series estimation")
  Member "on asymptotic properties of hyperparameter estimators for kernel based regularization methods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "wireless traffic prediction with scalable gaussian process framework algorithms and verification")
  Member "continuous time dc kernel a stable generalized first order spline kernel":
    also connected through other hyperedges:
      (none)
  Member "on input design for regularized lti system identification power constrained input":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "continuous time dc kernel a stable generalized first order spline kernel")
  Member "maximum entropy properties of discrete time first order stable spline kernel":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "high precision variational bayesian inference of sparse linear networks")
  Member "regularization and bayesian learning in dynamical systems past present and future":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on input design for regularized lti system identification power constrained input")
  Member "kernels for linear time invariant system identification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "high precision variational bayesian inference of sparse linear networks")

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.SY(Systems and Control)