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: "on asymptotic properties of hyperparameter estimators for kernel based regularization methods"
  Abstract: The kernel-based regularization method has two core issues: kernel design and hyperparameter estimation. In this paper, we focus on the second issue and study the properties of several hyperparameter estimators including the empirical Bayes (EB) estimator, two Stein's unbiased risk estimators (SURE) and their corresponding Oracle counterparts, with an emphasis on the asymptotic properties of these hyperparameter estimators. To this goal, we first derive and then rewrite the first order optimality conditions of these hyperparameter estimators, leading to several insights on these hyperparameter estimators. Then we show that as the number of data goes to infinity, the two SUREs converge to the best hyperparameter minimizing the corresponding mean square error, respectively, while the more widely used EB estimator converges to another best hyperparameter minimizing the expectation of the EB estimation criterion. This indicates that the two SUREs are asymptotically optimal but the EB estimator is not. Surprisingly, the convergence rate of two SUREs is slower than that of the EB estimator, and moreover, unlike the two SUREs, the EB estimator is independent of the convergence rate of $\Phi^T\Phi/N$ to its limit, where $\Phi$ is the regression matrix and $N$ is the number of data. A Monte Carlo simulation is provided to demonstrate the theoretical results.

Connected through hyperedge HE-1 (induced by source paper "wireless traffic prediction with scalable gaussian process framework algorithms and verification"):
  with members:
    - "green heterogeneous cloud radio access networks potential techniques performance trade offs and challenges"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"
    - "recent advances in cloud radio access networks system architectures key techniques and open issues"
    - "electricity demand forecasting by multi task learning"
    - "an approach for spatial temporal traffic modeling in mobile cellular networks"
    - "system architecture and key technologies for 5g heterogeneous cloud radio access networks"
    - "generalized product of experts for automatic and principled fusion of gaussian process predictions"

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 kernel design for regularized lti system identification"
    - "maximum entropy properties of discrete time first order stable spline kernel"
    - "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 "continuous time dc kernel a stable generalized first order spline kernel"):
  with members:
    - "on input design for regularized lti system identification power constrained input"
    - "on kernel design for regularized lti system identification"
    - "maximum entropy properties of discrete time first order stable spline kernel"

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