human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "subspace thresholding pursuit a reconstruction algorithm for compressed sensing"):
  Abstract: We propose a new iterative greedy algorithm for reconstructions of sparse signals with or without noisy perturbations in compressed sensing. The proposed algorithm, called \emph{subspace thresholding pursuit} (STP) in this paper, is a simple combination of subspace pursuit and iterative hard thresholding. Firstly, STP has the theoretical guarantee comparable to that of $\ell_1$ minimization in terms of restricted isometry property. Secondly, with a tuned parameter, on the one hand, when reconstructing Gaussian signals, it can outperform other state-of-the-art reconstruction algorithms greatly; on the other hand, when reconstructing constant amplitude signals with random signs, it can outperform other state-of-the-art iterative greedy algorithms and even outperform $\ell_1$ minimization if the undersampling ratio is not very large. In addition, we propose a simple but effective method to improve the empirical performance further if the undersampling ratio is large. Finally, it is showed that other iterative greedy algorithms can improve their empirical performance by borrowing the idea of STP.

Includes members:
  Member "improved analysis for subspace pursuit algorithm in terms of restricted isometry constant":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a new analysis for support recovery with block orthogonal matching pursuit")
  Member "sparse representation of a polytope and recovery of sparse signals and low rank matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the importance of phase in complex compressive sensing")
  Member "compressed sensing signal recovery via forward backward pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improving a omp")
  Member "sharp rip bound for sparse signal and low rank matrix recovery":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a deterministic convergence framework for exact non convex phase retrieval")
  Member "a remark on the restricted isometry property in orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "progressive stochastic greedy sparse reconstruction and support selection")
  Member "generalized orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "random access for massive machine type communications")
  Member "optimally tuned iterative reconstruction algorithms for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "random access for massive machine type communications")
  Member "phase transitions for greedy sparse approximation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "limits on sparse data acquisition ric analysis of finite gaussian matrices")

Question: Please tell me which class the source paper 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)