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 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.

Includes members:
  Member "continuous domain solutions of linear inverse problems with tikhonov versus generalized tv regularization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi kernel regression with sparsity constraint")
  Member "breaking the curse of dimensionality with convex neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "good subnetworks provably exist pruning via greedy forward selection")
  Member "nonparametric basis pursuit via sparse kernel based learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi kernel regression with sparsity constraint")
  Member "super resolution from noisy data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")

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.LG(Machine Learning)