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: "safe feature elimination in sparse supervised learning"
  Abstract: We investigate fast methods that allow to quickly eliminate variables (features) in supervised learning problems involving a convex loss function and a $l_1$-norm penalty, leading to a potentially substantial reduction in the number of variables prior to running the supervised learning algorithm. The methods are not heuristic: they only eliminate features that are {\em guaranteed} to be absent after solving the learning problem. Our framework applies to a large class of problems, including support vector machine classification, logistic regression and least-squares. #R##N#The complexity of the feature elimination step is negligible compared to the typical computational effort involved in the sparse supervised learning problem: it grows linearly with the number of features times the number of examples, with much better count if data is sparse. We apply our method to data sets arising in text classification and observe a dramatic reduction of the dimensionality, hence in computational effort required to solve the learning problem, especially when very sparse classifiers are sought. Our method allows to immediately extend the scope of existing algorithms, allowing us to run them on data sets of sizes that were out of their reach before.

Connected through hyperedge HE-1 (induced by source paper "safe squeezing for antisparse coding"):
  with members:
    - "screening rules for lasso with non convex sparse regularizers"
    - "screening tests for lasso problems"
    - "par aware large scale multi user mimo ofdm downlink"
    - "anti sparse coding for approximate nearest neighbor search"

Connected through hyperedge HE-2 (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"
    - "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems"

Connected through hyperedge HE-3 (induced by source paper "stable safe screening and structured dictionaries for faster ell _ 1 regularization"):
  with members:
    - "screening tests for lasso problems"
    - "joint screening tests for lasso"
    - "trainlets dictionary learning in high dimensions"
    - "flexible multilayer sparse approximations of matrices and applications"
    - "separable dictionary learning"
    - "safe screening with variational inequalities and its application to lasso"
    - "lasso screening rules via dual polytope projection"

Connected through hyperedge HE-4 (induced by source paper "screening tests for lasso problems"):
  with members:
    - "feedback controlled sequential lasso screening"
    - "safe screening with variational inequalities and its application to lasso"
    - "lasso screening rules via dual polytope projection"
    - "fast l1 minimization algorithms for robust face recognition"
    - "safe feature elimination for the lasso and sparse supervised learning problems"

Connected through hyperedge HE-5 (induced by source paper "feedback controlled sequential lasso screening"):
  with members:
    - "screening tests for lasso problems"
    - "the symmetry of a simple optimization problem in lasso screening"
    - "deep scattering spectrum"
    - "unsupervised feature learning by deep sparse coding"

Connected through hyperedge HE-6 (induced by source paper "the symmetry of a simple optimization problem in lasso screening"):
  with members:
    - "screening tests for lasso problems"
    - "feedback controlled sequential lasso screening"
    - "unsupervised feature learning by deep sparse coding"

Connected through hyperedge HE-7 (induced by source paper "safe screening for multi task feature learning with multiple data matrices"):
  with members:
    - "efficient mixed norm regularization algorithms and safe screening methods"
    - "scaling svm and least absolute deviations via exact data reduction"
    - "a safe screening rule for sparse logistic regression"
    - "lasso screening rules via dual polytope projection"
    - "a model of inductive bias learning"

Connected through hyperedge HE-8 (induced by source paper "conducting sparse feature selection on arbitrarily long phrases in text corpora with a focus on interpretability"):
  with members:
    - "concise comparative summaries ccs of large text corpora with a human experiment"
    - "bounded coordinate descent for biological sequence classification in high dimensional predictor space"

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)