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: "a complete analysis of the l_1 p group lasso"
  Abstract: The Group-Lasso is a well-known tool for joint regularization in machine learning methods. While the l_{1,2} and the l_{1,\infty} version have been studied in detail and efficient algorithms exist, there are still open questions regarding other l_{1,p} variants. We characterize conditions for solutions of the l_{1,p} Group-Lasso for all p-norms with 1  > 2.

Connected through hyperedge HE-1 (induced by source paper "correlated feature selection with extended exclusive group lasso"):
  with members:
    - "data dependent learning of symmetric antisymmetric relations for knowledge base completion"

Connected through hyperedge HE-2 (induced by source paper "robust dictionary based data representation"):
  with members:
    - "smoothed low rank and sparse matrix recovery by iteratively reweighted least squares minimization"
    - "robust and efficient subspace segmentation via least squares regression"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"

Connected through hyperedge HE-3 (induced by source paper "efficient mixed norm regularization algorithms and safe screening methods"):
  with members:
    - "lasso screening rules via dual polytope projection"
    - "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)