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: "screening tests for lasso problems"
  Abstract: This paper is a survey of dictionary screening for the lasso problem. The lasso problem seeks a sparse linear combination of the columns of a dictionary to best match a given target vector. This sparse representation has proven useful in a variety of subsequent processing and decision tasks. For a given target vector, dictionary screening quickly identifies a subset of dictionary columns that will receive zero weight in a solution of the corresponding lasso problem. These columns can be removed from the dictionary prior to solving the lasso problem without impacting the optimality of the solution obtained. This has two potential advantages: it reduces the size of the dictionary, allowing the lasso problem to be solved with less resources, and it may speed up obtaining a solution. Using a geometrically intuitive framework, we provide basic insights for understanding useful lasso screening tests and their limitations. We also provide illustrative numerical studies on several datasets.

Connected through hyperedge HE-1 (induced by source paper "tighter bound estimation of sensitivity analysis for incremental and decremental data modification"):
  with members:
    - "a safe screening rule for sparse logistic regression"
    - "safe screening with variational inequalities and its application to lasso"
    - "safe feature elimination for the lasso and sparse supervised learning problems"

Connected through hyperedge HE-2 (induced by source paper "safe squeezing for antisparse coding"):
  with members:
    - "screening rules for lasso with non convex sparse regularizers"
    - "par aware large scale multi user mimo ofdm downlink"
    - "anti sparse coding for approximate nearest neighbor search"
    - "safe feature elimination in sparse supervised learning"

Connected through hyperedge HE-3 (induced by source paper "estimating feature label dependence using gini distance statistics"):
  with members:
    - "a semidefinite programming based search strategy for feature selection with mutual information measure"
    - "a feature selection method for multivariate performance measures"
    - "feature selection with conjunctions of decision stumps and learning from microarray data"

Connected through hyperedge HE-4 (induced by source paper "stable safe screening and structured dictionaries for faster ell _ 1 regularization"):
  with members:
    - "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"
    - "safe feature elimination in sparse supervised learning"

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

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