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: "efficient sparse group feature selection via nonconvex optimization"
  Abstract: Sparse feature selection has been demonstrated to be effective in handling high-dimensional data. While promising, most of the existing works use convex methods, which may be suboptimal in terms of the accuracy of feature selection and parameter estimation. In this paper, we expand a nonconvex paradigm to sparse group feature selection, which is motivated by applications that require identifying the underlying group structure and performing feature selection simultaneously. The main contributions of this article are twofold: (1) statistically, we introduce a nonconvex sparse group feature selection model which can reconstruct the oracle estimator. Therefore, consistent feature selection and parameter estimation can be achieved; (2) computationally, we propose an efficient algorithm that is applicable to large-scale problems. Numerical results suggest that the proposed nonconvex method compares favorably against its competitors on synthetic data and real-world applications, thus achieving desired goal of delivering high performance.

Connected through hyperedge HE-1 (induced by source paper "robust subspace clustering via tighter rank approximation"):
  with members:
    - "logdet rank minimization with application to subspace clustering"
    - "generalized nonconvex nonsmooth low rank minimization"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "a geometric analysis of subspace clustering with outliers"
    - "exact subspace segmentation and outlier detection by low rank representation"
    - "the power of convex relaxation near optimal matrix completion"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-2 (induced by source paper "robust pca via nonconvex rank approximation"):
  with members:
    - "robust subspace clustering via tighter rank approximation"
    - "robust subspace clustering via smoothed rank approximation"
    - "non convex robust pca"
    - "robust recovery of subspace structures by low rank representation"
    - "robust pca via outlier pursuit"
    - "robust principal component analysis"

Connected through hyperedge HE-3 (induced by source paper "randomized structural sparsity via constrained block subsampling for improved sensitivity of discriminative voxel identification"):
  with members:
    - "small sample brain mapping sparse recovery on spatially correlated designs with randomization and clustering"
    - "a supervised clustering approach for fmri based inference of brain states"
    - "structured sparsity through convex optimization"
    - "optimization with sparsity inducing penalties"
    - "convergence rates of inexact proximal gradient methods for convex optimization"
    - "total variation regularization for fmri based prediction of behavior"

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)