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: "image reconstruction from few views by l0 norm optimization"
  Abstract: In the medical computer tomography (CT) field, total variation (TV), which is the l1-norm of the discrete gradient transform (DGT), is widely used as regularization based on the compressive sensing (CS) theory. To overcome the TV model's disadvantageous tendency of uniformly penalizing the image gradient and over smoothing the low-contrast structures, an iterative algorithm based on the l0-norm optimization of the DGT is proposed. In order to rise to the challenges introduced by the l0-norm DGT, the algorithm uses a pseudo-inverse transform of DGT and adapts an iterative hard thresholding (IHT) algorithm, whose convergence and effective efficiency have been theoretically proven. The simulation demonstrates our conclusions and indicates that the algorithm proposed in this paper can obviously improve the reconstruction quality.

Connected through hyperedge HE-1 (induced by source paper "sparse optimization problem with s difference regularization"):
  with members:
    - "iterative ell_1 minimization for non convex compressed sensing"
    - "compressed sensing recovery via nonconvex shrinkage penalties"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"
    - "minimization of transformed l_1 penalty theory difference of convex function algorithm and robust application in compressed sensing"
    - "decreasing weighted sorted ell_1 regularization"
    - "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems"
    - "iterative hard thresholding for compressed sensing"

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)