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 "optimization with sparsity inducing penalties"):
  Abstract: Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models. They were first dedicated to linear variable selection but numerous extensions have now emerged such as structured sparsity or kernel selection. It turns out that many of the related estimation problems can be cast as convex optimization problems by regularizing the empirical risk with appropriate non-smooth norms. The goal of this paper is to present from a general perspective optimization tools and techniques dedicated to such sparsity-inducing penalties. We cover proximal methods, block-coordinate descent, reweighted $\ell_2$-penalized techniques, working-set and homotopy methods, as well as non-convex formulations and extensions, and provide an extensive set of experiments to compare various algorithms from a computational point of view.

Includes members:
  Member "convex approaches to model wavelet sparsity patterns":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hybrid approximate message passing")
  Member "convergence rates of inexact proximal gradient methods for convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "credit assignment techniques in stochastic computation graphs")
  Member "robust pca via outlier pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "shaping level sets with submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fast decomposable submodular function minimization using constrained total variation")
  Member "network flow algorithms for structured sparsity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "regularized loss minimizers with local data perturbation consistency and data irrecoverability")
  Member "structured sparsity inducing norms through submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "safe screening for the generalized conditional gradient method")
  Member "model based compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "compressed sensing with invertible generative models and dependent noise")
  Member "collaborative hierarchical sparse modeling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hierarchical restricted isometry property for kronecker product measurements")

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)