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 screening with variational inequalities and its application to lasso"
  Abstract: Sparse learning techniques have been routinely used for feature selection as the resulting model usually has a small number of non-zero entries. Safe screening, which eliminates the features that are guaranteed to have zero coefficients for a certain value of the regularization parameter, is a technique for improving the computational efficiency. Safe screening is gaining increasing attention since 1) solving sparse learning formulations usually has a high computational cost especially when the number of features is large and 2) one needs to try several regularization parameters to select a suitable model. In this paper, we propose an approach called "Sasvi" (Safe screening with variational inequalities). Sasvi makes use of the variational inequality that provides the sufficient and necessary optimality condition for the dual problem. Several existing approaches for Lasso screening can be casted as relaxed versions of the proposed Sasvi, thus Sasvi provides a stronger safe screening rule. We further study the monotone properties of Sasvi for Lasso, based on which a sure removal regularization parameter can be identified for each feature. Experimental results on both synthetic and real data sets are reported to demonstrate the effectiveness of the proposed Sasvi for Lasso screening.

Connected through hyperedge HE-1 (induced by source paper "safe screening for the generalized conditional gradient method"):
  with members:
    - "deep frank wolfe for neural network optimization"
    - "multi task learning as multi objective optimization"
    - "joint screening tests for lasso"
    - "tight bounds for approximate carath eodory and beyond"
    - "the ordered weighted ell_1 norm atomic formulation projections and algorithms"
    - "a safe screening rule for sparse logistic regression"
    - "duality between subgradient and conditional gradient methods"
    - "block coordinate frank wolfe optimization for structural svms"

Connected through hyperedge HE-2 (induced by source paper "tighter bound estimation of sensitivity analysis for incremental and decremental data modification"):
  with members:
    - "screening tests for lasso problems"
    - "a safe screening rule for sparse logistic regression"
    - "safe feature elimination for the lasso and sparse supervised learning problems"

Connected through hyperedge HE-3 (induced by source paper "safe sample screening for robust support vector machine"):
  with members:
    - "screening rules for lasso with non convex sparse regularizers"
    - "scaling svm and least absolute deviations via exact data reduction"
    - "a safe screening rule for sparse logistic regression"
    - "safe feature elimination for the lasso and sparse supervised learning problems"

Connected through hyperedge HE-4 (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"
    - "lasso screening rules via dual polytope projection"
    - "safe feature elimination in sparse supervised learning"

Connected through hyperedge HE-5 (induced by source paper "screening tests for lasso problems"):
  with members:
    - "feedback controlled sequential lasso screening"
    - "lasso screening rules via dual polytope projection"
    - "safe feature elimination in sparse supervised learning"
    - "fast l1 minimization algorithms for robust face recognition"
    - "safe feature elimination for the lasso and sparse supervised learning problems"

Connected through hyperedge HE-6 (induced by source paper "safe adaptive importance sampling"):
  with members:
    - "faster coordinate descent via adaptive importance sampling"
    - "approximate steepest coordinate descent"
    - "importance sampling for minibatches"
    - "minding the gaps for block frank wolfe optimization of structured svms"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"

Connected through hyperedge HE-7 (induced by source paper "successive ray refinement and its application to coordinate descent for lasso"):
  with members:
    - "safe feature elimination in sparse supervised learning"

Connected through hyperedge HE-8 (induced by source paper "two layer feature reduction for sparse group lasso via decomposition of convex sets"):
  with members:
    - "efficient mixed norm regularization algorithms and safe screening methods"
    - "scaling svm and least absolute deviations via exact data reduction"
    - "safe feature elimination in sparse supervised learning"

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)