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: "improved analysis for subspace pursuit algorithm in terms of restricted isometry constant"
  Abstract: In the context of compressed sensing (CS), both Subspace Pursuit (SP) and Compressive Sampling Matching Pursuit (CoSaMP) are very important iterative greedy recovery algorithms which could reduce the recovery complexity greatly comparing with the well-known $\ell_1$-minimization. Restricted isometry property (RIP) and restricted isometry constant (RIC) of measurement matrices which ensure the convergency of iterative algorithms play key roles for the guarantee of successful reconstructions. In this paper, we show that for the $s$-sparse recovery, the RICs are enlarged to $\delta_{3s}<0.4859$ for SP and $\delta_{4s}<0.5$ for CoSaMP, which improve the known results significantly. The proposed results also apply to almost sparse signal and corrupted measurements.

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

Connected through hyperedge HE-2 (induced by source paper "alternating direction algorithms for ell_0 regularization in compressed sensing"):
  with members:
    - "optimally tuned iterative reconstruction algorithms for compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "on the number of iterations for convergence of cosamp and sp algorithm"):
  with members:
    - "oblique pursuits for compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-4 (induced by source paper "on the number of iterations for convergence of cosamp and subspace pursuit algorithms"):
  with members:
    - "oblique pursuits for compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-5 (induced by source paper "subspace thresholding pursuit a reconstruction algorithm for compressed sensing"):
  with members:
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "compressed sensing signal recovery via forward backward pursuit"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "generalized orthogonal matching pursuit"
    - "optimally tuned iterative reconstruction algorithms for compressed sensing"
    - "phase transitions for greedy sparse approximation algorithms"
    - "subspace pursuit for compressive sensing signal reconstruction"

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)