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: "phase transitions for greedy sparse approximation algorithms"
  Abstract: A major enterprise in compressed sensing and sparse approximation is the design and analysis of computationally tractable algorithms for recovering sparse, exact or approximate, solutions of underdetermined linear systems of equations. Many such algorithms have now been proven to have optimal-order uniform recovery guarantees using the ubiquitous Restricted Isometry Property (RIP). However, it is unclear when the RIP-based sufficient conditions on the algorithm are satisfied. We present a framework in which this task can be achieved; translating these conditions for Gaussian measurement matrices into requirements on the signal's sparsity level, length, and number of measurements. We illustrate this approach on three of the state-of-the-art greedy algorithms: CoSaMP, Subspace Pursuit (SP), and Iterative Hard Thresholding (IHT). Designed to allow a direct comparison of existing theory, our framework implies that, according to the best known bounds, IHT requires the fewest number of compressed sensing measurements and has the lowest per iteration computational cost of the three algorithms compared here.

Connected through hyperedge HE-1 (induced by source paper "limits on sparse data acquisition ric analysis of finite gaussian matrices"):
  with members:
    - "d oamp a denoising based signal recovery algorithm for compressed sensing"
    - "graded quantization for multiple description coding of compressive measurements"
    - "refined analysis of sparse mimo radar"
    - "operational rate distortion performance of single source and distributed compressed sensing"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "improved bounds on restricted isometry constants for gaussian matrices"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "sparse recovery with orthogonal matching pursuit under rip"

Connected through hyperedge HE-2 (induced by source paper "quantitative recovery conditions for tree based compressed sensing"):
  with members:
    - "structured sparsity discrete and convex approaches"
    - "approximation algorithms for model based compressive sensing"
    - "combinatorial selection and least absolute shrinkage via the clash algorithm"
    - "learning with submodular functions a convex optimization perspective"
    - "k median clustering model based compressive sensing and sparse recovery for earth mover distance"
    - "compressed sensing how sharp is the restricted isometry property"
    - "model based compressive sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-3 (induced by source paper "the gap between the null space property and the restricted isometry property"):
  with members:
    - "the restricted isometry property of subsampled fourier matrices"
    - "on the gap between rip properties and sparse recovery conditions"
    - "robust width a characterization of uniformly stable and robust compressed sensing"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "a null space analysis of the l1 synthesis method in dictionary based compressed sensing"
    - "stable and robust sampling strategies for compressive imaging"
    - "sparse approximation property and stable recovery of sparse signals from noisy measurements"

Connected through hyperedge HE-4 (induced by source paper "restricted isometry random variables probability distributions ric prediction and phase transition analysis for gaussian encoders"):
  with members:
    - "optimal phase transitions in compressed sensing"
    - "improved bounds on restricted isometry constants for gaussian matrices"
    - "restricted isometries for partial random circulant matrices"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-5 (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"
    - "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"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-6 (induced by source paper "on construction and analysis of sparse random matrices and expander graphs with applications to compressed sensing"):
  with members:
    - "vanishingly sparse matrices and expander graphs with application to compressed sensing"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-7 (induced by source paper "the restricted isometry property for random block diagonal matrices"):
  with members:
    - "fast and efficient compressive sensing using structurally random matrices"
    - "improved bounds on restricted isometry constants for gaussian matrices"
    - "compressed sensing how sharp is the restricted isometry property"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"
    - "restricted isometries for partial random circulant matrices"
    - "stable takens embeddings for linear dynamical systems"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-8 (induced by source paper "restricted isometry property in quantized network coding of sparse messages"):
  with members:
    - "quantized network coding for sparse messages"
    - "a power efficient sensing communication scheme joint source channel network coding by using compressive sensing"
    - "improved bounds on restricted isometry constants for gaussian matrices"
    - "compressed sensing how sharp is the restricted isometry property"
    - "compressive sensing over networks"
    - "network information flow with correlated sources"

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)