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: "kernels for linear time invariant system identification"
  Abstract: In this paper, we study the problem of identifying the impulse response of a linear time invariant (LTI) dynamical system from the knowledge of the input signal and a finite set of noisy output observations. We adopt an approach based on regularization in a Reproducing Kernel Hilbert Space (RKHS) that takes into account both continuous and discrete time systems. The focus of the paper is on designing spaces that are well suited for temporal impulse response modeling. To this end, we construct and characterize general families of kernels that incorporate system properties such as stability, relative degree, absence of oscillatory behavior, smoothness, or delay. In addition, we discuss the possibility of automatically searching over these classes by means of kernel learning techniques, so as to capture different modes of the system to be identified.

Connected through hyperedge HE-1 (induced by source paper "high precision variational bayesian inference of sparse linear networks"):
  with members:
    - "maximum entropy properties of discrete time first order stable spline kernel"
    - "robust em kernel based methods for linear system identification"
    - "neural variational inference and learning in belief networks"

Connected through hyperedge HE-2 (induced by source paper "a full bayesian approach to sparse network inference using heterogeneous datasets"):
  with members:
    - "robust em kernel based methods for linear system identification"
    - "reversible jump mcmc simulated annealing for neural networks"

Connected through hyperedge HE-3 (induced by source paper "kernel absolute summability is only sufficient for rkhs stability"):
  with members:
    - "on kernel design for regularized lti system identification"

Connected through hyperedge HE-4 (induced by source paper "on asymptotic properties of hyperparameter estimators for kernel based regularization methods"):
  with members:
    - "filter based regularisation for impulse response modelling"
    - "on kernel design for regularized lti system identification"
    - "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:
    - "on kernel design for regularized lti system identification"

Connected through hyperedge HE-6 (induced by source paper "on kernel design for regularized lti system identification"):
  with members:
    - "filter based regularisation for impulse response modelling"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "regularization and bayesian learning in dynamical systems past present and future"):
  with members:
    - "n2sid nuclear norm subsystem identification"
    - "maximum entropy properties of discrete time first order stable spline kernel"
    - "approximate regularization paths for nuclear norm minimization using singular value bounds with implementation and extended appendix"
    - "regularized system identification using orthonormal basis functions"
    - "bayesian and regularization approaches to multivariable linear system identification the role of rank penalties"
    - "relevance singular vector machine for low rank matrix sensing"

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)