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: "maximum entropy properties of discrete time first order stable spline kernel"
  Abstract: The first order stable spline (SS-1) kernel is used extensively in regularized system identification. In particular, the stable spline estimator models the impulse response as a zero-mean Gaussian process whose covariance is given by the SS-1 kernel. In this paper, we discuss the maximum entropy properties of this prior. In particular, we formulate the exact maximum entropy problem solved by the SS-1 kernel without Gaussian and uniform sampling assumptions. Under general sampling schemes, we also explicitly derive the special structure underlying the SS-1 kernel (e.g. characterizing the tridiagonal nature of its inverse), also giving to it a maximum entropy covariance completion interpretation. Along the way similar maximum entropy properties of the Wiener kernel are also given.

Connected through hyperedge HE-1 (induced by source paper "high precision variational bayesian inference of sparse linear networks"):
  with members:
    - "robust em kernel based methods for linear system identification"
    - "neural variational inference and learning in belief networks"
    - "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"
    - "on kernel design for regularized lti system identification"
    - "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"
    - "on kernel design for regularized lti system identification"
    - "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"
    - "on kernel design for regularized lti system identification"

Connected through hyperedge HE-5 (induced by source paper "filter based regularisation for impulse response modelling"):
  with members:
    - "regularized system identification using orthonormal basis functions"

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"
    - "regularization and bayesian learning in dynamical systems past present and future"
    - "kernels for linear time invariant system identification"

Connected through hyperedge HE-7 (induced by source paper "a new kernel based approach to system identification with quantized output data"):
  with members:
    - "bayesian kernel based system identification with quantized output data"
    - "robust em kernel based methods for linear system identification"

Connected through hyperedge HE-8 (induced by source paper "regularization and bayesian learning in dynamical systems past present and future"):
  with members:
    - "n2sid nuclear norm subsystem identification"
    - "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"
    - "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)