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: "lasso screening rules via dual polytope projection"
  Abstract: Lasso is a widely used regression technique to find sparse representations. When the dimension of the feature space and the number of samples are extremely large, solving the Lasso problem remains challenging. To improve the efficiency of solving large-scale Lasso problems, El Ghaoui and his colleagues have proposed the SAFE rules which are able to quickly identify the inactive predictors, i.e., predictors that have $0$ components in the solution vector. Then, the inactive predictors or features can be removed from the optimization problem to reduce its scale. By transforming the standard Lasso to its dual form, it can be shown that the inactive predictors include the set of inactive constraints on the optimal dual solution. In this paper, we propose an efficient and effective screening rule via Dual Polytope Projections (DPP), which is mainly based on the uniqueness and nonexpansiveness of the optimal dual solution due to the fact that the feasible set in the dual space is a convex and closed polytope. Moreover, we show that our screening rule can be extended to identify inactive groups in group Lasso. To the best of our knowledge, there is currently no "exact" screening rule for group Lasso. We have evaluated our screening rule using synthetic and real data sets. Results show that our rule is more effective in identifying inactive predictors than existing state-of-the-art screening rules for Lasso.

Connected through hyperedge HE-1 (induced by source paper "sparse subspace clustering via two step reweighted l1 minimization algorithm and provable neighbor recovery rates"):
  with members:
    - "noisy subspace clustering via matching pursuits"
    - "iteratively reweighted ell_1 approaches to sparse composite regularization"
    - "robust subspace clustering"
    - "greedy feature selection for subspace clustering"
    - "a geometric analysis of subspace clustering with outliers"
    - "improving the thresholds of sparse recovery an analysis of a two step reweighted basis pursuit algorithm"
    - "recovering compressively sampled signals using partial support information"

Connected through hyperedge HE-2 (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"
    - "safe feature elimination in sparse supervised learning"

Connected through hyperedge HE-3 (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"
    - "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-4 (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"
    - "safe feature elimination in sparse supervised learning"
    - "a model of inductive bias learning"

Connected through hyperedge HE-5 (induced by source paper "efficient mixed norm regularization algorithms and safe screening methods"):
  with members:
    - "a complete analysis of the l_1 p group lasso"
    - "safe feature elimination in sparse supervised learning"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "sparse online learning via truncated gradient"
    - "consistency of the group lasso and multiple kernel 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)