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: "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems"
  Abstract: Non-convex sparsity-inducing penalties have recently received considerable attentions in sparse learning. Recent theoretical investigations have demonstrated their superiority over the convex counterparts in several sparse learning settings. However, solving the non-convex optimization problems associated with non-convex penalties remains a big challenge. A commonly used approach is the Multi-Stage (MS) convex relaxation (or DC programming), which relaxes the original non-convex problem to a sequence of convex problems. This approach is usually not very practical for large-scale problems because its computational cost is a multiple of solving a single convex problem. In this paper, we propose a General Iterative Shrinkage and Thresholding (GIST) algorithm to solve the nonconvex optimization problem for a large class of non-convex penalties. The GIST algorithm iteratively solves a proximal operator problem, which in turn has a closed-form solution for many commonly used penalties. At each outer iteration of the algorithm, we use a line search initialized by the Barzilai-Borwein (BB) rule that allows finding an appropriate step size quickly. The paper also presents a detailed convergence analysis of the GIST algorithm. The efficiency of the proposed algorithm is demonstrated by extensive experiments on large-scale data sets.

Connected through hyperedge HE-1 (induced by source paper "binary matrix completion with nonconvex regularizers"):
  with members:
    - "link prediction via matrix completion"
    - "positive unlabeled learning with non negative risk estimator"
    - "large scale low rank matrix learning with nonconvex regularizers"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "matrix completion via max norm constrained optimization"
    - "semi supervised classification based on classification from positive and unlabeled data"
    - "network topology inference from spectral templates"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"

Connected through hyperedge HE-2 (induced by source paper "investigating task driven latent feasibility for nonconvex image modeling"):
  with members:
    - "fastderain a novel video rain streak removal method using directional gradient priors"
    - "plug and play methods provably converge with properly trained denoisers"
    - "deep plug and play super resolution for arbitrary blur kernels"
    - "regularization by denoising clarifications and new interpretations"
    - "on the convergence of learning based iterative methods for nonconvex inverse problems"
    - "acceleration of red via vector extrapolation"
    - "convergence analysis of proximal gradient with momentum for nonconvex optimization"
    - "clearing the skies a deep network architecture for single image rain removal"

Connected through hyperedge HE-3 (induced by source paper "sparse optimization problem with s difference regularization"):
  with members:
    - "iterative ell_1 minimization for non convex compressed sensing"
    - "compressed sensing recovery via nonconvex shrinkage penalties"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"
    - "image reconstruction from few views by l0 norm optimization"
    - "minimization of transformed l_1 penalty theory difference of convex function algorithm and robust application in compressed sensing"
    - "decreasing weighted sorted ell_1 regularization"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "nonconvex approach for sparse and low rank constrained models with dual momentum"):
  with members:
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "generalized singular value thresholding"
    - "robust recovery of subspace structures by low rank representation"
    - "robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "non local low rank cube based tensor factorization for spectral ct reconstruction"):
  with members:
    - "weighted low rank tensor recovery for hyperspectral image restoration"
    - "generalized singular value thresholding"
    - "tensor based formulation and nuclear norm regularization for multienergy computed tomography"
    - "robust principal component analysis"

Connected through hyperedge HE-6 (induced by source paper "an iteratively re weighted method for problems with sparsity inducing norms"):
  with members:
    - "generalized nonconvex nonsmooth low rank minimization"
    - "robust recovery of subspace structures by low rank representation"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-7 (induced by source paper "screening rules for lasso with non convex sparse regularizers"):
  with members:
    - "on the global convergence of majorization minimization algorithms for nonconvex optimization problems"
    - "safe feature elimination in sparse supervised learning"

Connected through hyperedge HE-8 (induced by source paper "parameter estimation with the ordered 120001 2 regularization via an alternating direction method of multipliers"):
  with members:
    - "decreasing weighted sorted ell_1 regularization"
    - "convergence rates of inexact proximal gradient methods for convex optimization"

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)