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: "robust em kernel based methods for linear system identification"
  Abstract: Recent developments in system identification have brought attention to regularized kernel-based methods. This type of approach has been proven to compare favorably with classic parametric methods. However, current formulations are not robust with respect to outliers. In this paper, we introduce a novel method to robustify kernel-based system identification methods. To this end, we model the output measurement noise using random variables with heavy-tailed probability density functions (pdfs), focusing on the Laplacian and the Student's t distributions. Exploiting the representation of these pdfs as scale mixtures of Gaussians, we cast our system identification problem into a Gaussian process regression framework, which requires estimating a number of hyperparameters of the data size order. To overcome this difficulty, we design a new maximum a posteriori (MAP) estimator of the hyperparameters, and solve the related optimization problem with a novel iterative scheme based on the Expectation-Maximization (EM) method. In presence of outliers, tests on simulated data and on a real system show a substantial performance improvement compared to currently used kernel-based methods for linear system identification.

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"
    - "neural variational inference and learning in belief networks"
    - "kernels for linear time invariant system identification"

Connected through hyperedge HE-2 (induced by source paper "bayesian topology identification of linear dynamic networks"):
  with members:
    - "prediction error identification of linear dynamic networks with rank reduced noise"
    - "topology identification of directed dynamical networks via power spectral analysis"
    - "block sparse signals uncertainty relations and efficient recovery"

Connected through hyperedge HE-3 (induced by source paper "a full bayesian approach to sparse network inference using heterogeneous datasets"):
  with members:
    - "reversible jump mcmc simulated annealing for neural networks"
    - "kernels for linear time invariant system identification"

Connected through hyperedge HE-4 (induced by source paper "an empirical bayes approach to identification of modules in dynamic networks"):
  with members:
    - "optimal model order reduction with the steiglitz mcbride method for open loop data"
    - "variance analysis of linear simo models with spatially correlated noise"
    - "bayesian and regularization approaches to multivariable linear system identification the role of rank penalties"
    - "julia a fresh approach to numerical computing"
    - "subspace identification of large scale interconnected systems"

Connected through hyperedge HE-5 (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"
    - "maximum entropy properties of discrete time first order stable spline kernel"

Connected through hyperedge HE-6 (induced by source paper "online identification of time varying systems a bayesian approach"):
  with members:
    - "on line bayesian system identification"
    - "blind system identification using kernel based methods"

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)