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: "compressed sensing signal recovery via forward backward pursuit"
  Abstract: Recovery of sparse signals from compressed measurements constitutes an @?"0 norm minimization problem, which is unpractical to solve. A number of sparse recovery approaches have appeared in the literature, including @?"1 minimization techniques, greedy pursuit algorithms, Bayesian methods and nonconvex optimization techniques among others. This manuscript introduces a novel two stage greedy approach, called the Forward-Backward Pursuit (FBP). FBP is an iterative approach where each iteration consists of consecutive forward and backward stages. The forward step first expands the support estimate by the forward step size, while the following backward step shrinks it by the backward step size. The forward step size is larger than the backward step size, hence the initially empty support estimate is expanded at the end of each iteration. Forward and backward steps are iterated until the residual power of the observation vector falls below a threshold. This structure of FBP does not necessitate the sparsity level to be known a priori in contrast to the Subspace Pursuit or Compressive Sampling Matching Pursuit algorithms. FBP recovery performance is demonstrated via simulations including recovery of random sparse signals with different nonzero coefficient distributions in noisy and noise-free scenarios in addition to the recovery of a sparse image.

Connected through hyperedge HE-1 (induced by source paper "improving a omp"):
  with members:
    - "distributed greedy pursuit algorithms"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "a orthogonal matching pursuit best first search for compressed sensing signal recovery"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "sparse signal reconstruction via iterative support detection"
    - "optimally tuned iterative reconstruction algorithms for compressed sensing"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-2 (induced by source paper "a survey of sparse representation algorithms and applications"):
  with members:
    - "group based sparse representation for image restoration"
    - "l_ 1 2 regularization convergence of iterative half thresholding algorithm"
    - "constructing a non negative low rank and sparse graph with data adaptive features"
    - "sparse recovery of streaming signals using l1 homotopy"
    - "an mdl framework for sparse coding and dictionary learning"
    - "image super resolution via dual dictionary learning and sparse representation"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"
    - "fast l1 minimization algorithms for robust face recognition"

Connected through hyperedge HE-3 (induced by source paper "subspace thresholding pursuit a reconstruction algorithm for compressed sensing"):
  with members:
    - "improved analysis for subspace pursuit algorithm in terms of restricted isometry constant"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "online recovery guarantees and analytical results for omp"):
  with members:
    - "generalized orthogonal matching pursuit"
    - "a orthogonal matching pursuit best first search for compressed sensing signal recovery"
    - "structured compressed sensing from theory to applications"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "model based compressive sensing"
    - "optimally tuned iterative reconstruction algorithms for compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "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)