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: "an l1 representer theorem for multiple kernel regression"
  Abstract: The theory of RKHS provides an elegant framework for supervised learning. It is the foundation of all kernel methods in machine learning. Implicit in its formulation is the use of a quadratic regularizer associated with the underlying inner product which imposes smoothness constraints. In this paper, we consider instead the generalized total-variation (gTV) norm as the sparsity-promoting regularizer. This leads us to propose a new Banach-space framework that justifies the use of generalized LASSO, albeit in a slightly modified version. We prove a representer theorem for multiple-kernel regression (MKR) with gTV regularization. The theorem states that the solutions of MKR have kernel expansions with adaptive positions, while the gTV norm enforces an $\ell_1$ penalty on the coefficients. We discuss the sparsity-promoting effect of the gTV norm which prevents redundancy in the multiple-kernel scenario.

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)