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: "approximate projected generalized gradient methods with sparsity inducing penalties"
  Abstract: Projected gradient (or subgradient) methods are simple and typical approaches to minimize a convex function with constraints. In the area of sparse recovery, however, plenty research results reveal that non-convex penalties induce better sparsity than convex ones. In this paper, the idea of projected subgradient methods is extended to minimize a class of sparsity-inducing penalties with linear constraints, and these penalties are not necessarily convex. To make the algorithm computationally tractable for large scale problems, a uniform approximate projection is applied in the projection step. The theoretical convergence analysis of the proposed method, approximate projected generalized gradient (APGG) method, is provided in the noisy scenario. The result reveals that if the initial solution satisfies some certain requirements, the bound of the recovery error is linear in both the noise term and the step size of APGG. In addition, the parameter selection rules and the initial criteria are analyzed. If the approximate least squares solution is adopted as the initial one, the result reveals how non-convex the penalty could be to guarantee convergence to the global optimal solution. Numerical simulations are performed to test the performance of the proposed method and verify the theoretical analysis. Contributions of this paper are compared with some existing results in the end.

Connected through hyperedge HE-1 (induced by source paper "robustness of sparse recovery via f minimization a topological viewpoint"):
  with members:
    - "living on the edge phase transitions in convex programs with random data"
    - "the convergence guarantees of a non convex approach for sparse recovery"
    - "proof of convergence and performance analysis for sparse recovery via zero point attracting projection"
    - "optimal phase transitions in compressed sensing"
    - "compressed sensing how sharp is the restricted isometry property"
    - "a stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework"
    - "on the stable recovery of the sparsest overcomplete representations in presence of noise"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"

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.IT(Information Theory)