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 "an empirical bayes approach to identification of modules in dynamic networks"):
  Abstract: We present a new method of identifying a specific module in a dynamic network, possibly with feedback loops. Assuming known topology, we express the dynamics by an acyclic network composed of two blocks where the first block accounts for the relation between the known reference signals and the input to the target module, while the second block contains the target module. Using an empirical Bayes approach, we model the first block as a Gaussian vector with covariance matrix (kernel) given by the recently introduced stable spline kernel. The parameters of the target module are estimated by solving a marginal likelihood problem with a novel iterative scheme based on the Expectation-Maximization algorithm. Additionally, we extend the method to include additional measurements downstream of the target module. Using Markov Chain Monte Carlo techniques, it is shown that the same iterative scheme can solve also this formulation. Numerical experiments illustrate the effectiveness of the proposed methods.

Includes members:
  Member "optimal model order reduction with the steiglitz mcbride method for open loop data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "estimating models with high order noise dynamics using semi parametric weighted null space fitting")
  Member "variance analysis of linear simo models with spatially correlated noise":
    also connected through other hyperedges:
      (none)
  Member "bayesian and regularization approaches to multivariable linear system identification the role of rank penalties":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "regularization and bayesian learning in dynamical systems past present and future")
  Member "robust em kernel based methods for linear system identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "high precision variational bayesian inference of sparse linear networks")
  Member "julia a fresh approach to numerical computing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the deep learning compiler a comprehensive survey")
  Member "subspace identification of large scale interconnected systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "prediction error identification of linear dynamic networks with rank reduced noise")

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)