human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "feedback controlled sequential lasso screening":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the symmetry of a simple optimization problem in lasso screening")
  Member "safe screening with variational inequalities and its application to lasso":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "safe screening for the generalized conditional gradient method")
  Member "lasso screening rules via dual polytope projection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparse subspace clustering via two step reweighted l1 minimization algorithm and provable neighbor recovery rates")
  Member "safe feature elimination in sparse supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "safe squeezing for antisparse coding")
  Member "fast l1 minimization algorithms for robust face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey of sparse representation algorithms and applications")
  Member "safe feature elimination for the lasso and sparse supervised learning problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "safe screening for the generalized conditional gradient method")

Question: Please tell me which class the source paper 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)