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 non linear regression analysis a greedy approach employing kernels and application to image denoising"
  Abstract: We consider the task of robust non-linear estimation in the presence of both bounded noise and outliers. Assuming that the unknown non-linear function belongs to a Reproducing Kernel Hilbert Space (RKHS), our goal is to accurately estimate the coefficients of the kernel regression matrix. Due to the existence of outliers, common techniques such as the Kernel Ridge Regression (KRR), or the Support Vector Regression (SVR) turn out to be inadequate. Instead, we employ sparse modeling arguments to model and estimate the outliers, adopting a greedy approach. In particular, the proposed robust scheme, i.e., Kernel Greedy Algorithm for Robust Denoising (KGARD), is a modification of the classical Orthogonal Matching Pursuit (OMP) algorithm. In a nutshell, the proposed scheme alternates between a KRR task and an OMP-like selection step. Convergence properties as well as theoretical results concerning the identification of the outliers are provided. Moreover, KGARD is compared against other cutting edge methods (using toy examples) to demonstrate its performance and verify the aforementioned theoretical results. Finally, the proposed robust estimation framework is applied to the task of image denoising, showing that it can enhance the denoising process significantly, when outliers are present.

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.LG(Machine Learning)