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 sharp recovery condition for block sparse signals by block orthogonal multi matching pursuit"
  Abstract: We consider the block orthogonal multi-matching pursuit (BOMMP) algorithm for the recovery of block sparse signals. A sharp condition is obtained for the exact reconstruction of block K-sparse signals via the BOMMP algorithm in the noiseless case, based on the block restricted isometry constant (block-RIC). Moreover, we show that the sharp condition combining with an extra condition on the minimum l2 norm of nonzero blocks of block K-sparse signals is sufficient to ensure the BOMMP algorithm selects at least one true block index at each iteration until all true block indices are selected in the noisy case. The significance of the results we obtain in this paper lies in the fact that making explicit use of block sparsity of block sparse signals can achieve better recovery performance than ignoring the additional structure in the problem as being in the conventional sense.

Connected through hyperedge HE-1 (induced by source paper "a new analysis for support recovery with block orthogonal matching pursuit"):
  with members:
    - "sharp sufficient conditions for stable recovery of block sparse signals by block orthogonal matching pursuit"
    - "nearly optimal bounds for orthogonal least squares"
    - "improved analysis for subspace pursuit algorithm in terms of restricted isometry constant"
    - "block sparse signals uncertainty relations and efficient recovery"

Connected through hyperedge HE-2 (induced by source paper "sharp sufficient conditions for stable recovery of block sparse signals by block orthogonal matching pursuit"):
  with members:
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "sharp conditions for exact support recovery of sparse signals with noise via omp"
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "relaxed recovery conditions for omp ols by exploiting both coherence and decay"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "perturbation analysis of orthogonal matching pursuit"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "the performance of orthogonal multi matching pursuit under rip"

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)