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: "nonconvex regularizations for feature selection in ranking with sparse svm"
  Abstract: Feature selection in learning to rank has recently emerged as a crucial issue. Whereas several preprocessing approaches have been proposed, only a few have focused on integrating feature selection into the learning process. In this paper, we propose a general framework for feature selection in learning to rank using support vector machines with a sparse regularization term. We investigate both classical convex regularizations, such as    $\ell_{1}$    or weighted    $\ell_{1}$   , and nonconvex regularization terms, such as log penalty, minimax concave penalty, or    $\ell_{p}$    pseudo-norm with    $p\!\!  . Two algorithms are proposed: the first, an accelerated proximal approach for solving the convex problems, and, the second, a reweighted    $\ell_{1}$    scheme to address nonconvex regularizations. We conduct intensive experiments on nine datasets from Letor 3.0 and Letor 4.0 corpora. Numerical results show that the use of nonconvex regularizations we propose leads to more sparsity in the resulting models while preserving the prediction performance. The number of features is decreased by up to a factor of 6 compared to the    $\ell_{1}$    regularization. In addition, the software is publicly available on the web.  1   Available at  http://remi.flamary.com/soft/soft-ranksvm-nc.html .

Connected through hyperedge HE-1 (induced by source paper "feature selection using stochastic gates"):
  with members:
    - "optuna a next generation hyperparameter optimization framework"
    - "implicit reparameterization gradients"
    - "large scale nonlinear variable selection via kernel random features"
    - "dropout feature ranking for deep learning models"
    - "xgboost a scalable tree boosting system"
    - "why should i trust you explaining the predictions of any classifier"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "variational inference for monte carlo objectives"

Connected through hyperedge HE-2 (induced by source paper "a review on ranking problems in statistical learning"):
  with members:
    - "sodeep a sorting deep net to learn ranking loss surrogates"
    - "training deep neural networks via direct loss minimization"
    - "sparse support vector infinite push"

Connected through hyperedge HE-3 (induced by source paper "sparse hierarchical interaction learning with epigraphical projection"):
  with members:
    - "playing with duality an overview of recent primal dual approaches for solving large scale optimization problems"
    - "invariant scattering convolution networks"
    - "structured sparsity through convex optimization"
    - "convergence rates of inexact proximal gradient methods for convex optimization"

Connected through hyperedge HE-4 (induced by source paper "stochastic variance reduction gradient for a non convex problem using graduated optimization"):
  with members:
    - "sparse algorithm for robust lssvm in primal space"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "a proximal approach for sparse multiclass svm"):
  with members:
    - "playing with duality an overview of recent primal dual approaches for solving large scale optimization problems"
    - "invariant scattering convolution networks"
    - "optimization with sparsity inducing penalties"

Connected through hyperedge HE-6 (induced by source paper "dc proximal newton for non convex optimization problems"):
  with members:
    - "low rank structure learning via nonconvex heuristic recovery"
    - "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems"
    - "estimation of simultaneously sparse and low rank matrices"

Connected through hyperedge HE-7 (induced by source paper "greedy methods randomization approaches and multi arm bandit algorithms for efficient sparsity constrained optimization"):
  with members:
    - "non stochastic best arm identification and hyperparameter optimization"
    - "dc proximal newton for non convex optimization problems"
    - "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems"

Connected through hyperedge HE-8 (induced by source paper "importance sampling strategy for non convex randomized block coordinate descent"):
  with members:
    - "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems"

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)