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: "on ric bounds of compressed sensing matrices for approximating sparse solutions using l q quasi norms"
  Abstract: This paper follows the recent discussion on the sparse solution recovery with quasi-norms lq, q 2 (0,1) when the sensing matrix possesses a Restricted Isometry Constant δ2k (RIC). Our key tool is an improvement on a version of “the converse of a generalized Cauchy-Schwarz inequality” extended to the setting of quasi-norm. We show that, if δ2k � 1/2, any minimizer of the lq minimization, at least for those q 2 (0,0.9181], is the sparse solution of the corresponding underdetermined linear system. Moreover, if δ2k � 0.4931, the sparse solution can be recovered by any lq,q 2 (0,1) minimization. The values 0.9181 and 0.4931 improves those reported previously in the literature.

Connected through hyperedge HE-1 (induced by source paper "performance guarantees for schatten p quasi norm minimization in recovery of low rank matrices"):
  with members:
    - "sparse signal recovery by ell_q minimization under restricted isometry property"
    - "doa estimation in partially correlated noise using low rank sparse matrix decomposition"
    - "calibration using matrix completion with application to ultrasound tomography"
    - "new ric bounds via l_q minimization with 0 q 1 in compressed sensing"
    - "matrix completion in colocated mimo radar recoverability bounds theoretical guarantees"
    - "collaborative spectrum sensing from sparse observations in cognitive radio networks"
    - "matrix completion with noise"
    - "sparse recovery by non convex optimization instance optimality"

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)