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 input design for regularized lti system identification power constrained input"):
  Abstract: Input design is an important issue for classical system identification methods but has not been investigated for the kernel-based regularization method (KRM) until very recently. In this paper, we consider in the time domain the input design problem of KRMs for LTI system identification. Different from the recent result, we adopt a Bayesian perspective and in particular make use of scalar measures (e.g., the $A$-optimality, $D$-optimality, and $E$-optimality) of the Bayesian mean square error matrix as the design criteria subject to power-constraint on the input. Instead to solve the optimization problem directly, we propose a two-step procedure. In the first step, by making suitable assumptions on the unknown input, we construct a quadratic map (transformation) of the input such that the transformed input design problems are convex, the number of optimization variables is independent of the number of input data, and their global minima can be found efficiently by applying well-developed convex optimization software packages. In the second step, we derive the expression of the optimal input based on the global minima found in the first step by solving the inverse image of the quadratic map. In addition, we derive analytic results for some special types of fixed kernels, which provide insights on the input design and also its dependence on the kernel structure.

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 "on kernel design for regularized lti system identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "kernel absolute summability is only sufficient for rkhs stability")
  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 kernel design for regularized lti system identification")
  Member "regularized system identification using orthonormal basis functions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "filter based regularisation for impulse response modelling")

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)