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 "near optimal compressed sensing of a class of sparse low rank matrices via sparse power factorization"):
  Abstract: Compressed sensing of simultaneously sparse and low-rank matrices enables recovery of sparse signals from a few linear measurements of their bilinear form. One important question is how many measurements are needed for a stable reconstruction in the presence of measurement noise. Unlike conventional compressed sensing for sparse vectors, where convex relaxation via the $\ell_1$-norm achieves near optimal performance, for compressed sensing of sparse low-rank matrices, it has been shown recently Oymak et al. that convex programmings using the nuclear norm and the mixed norm are highly suboptimal even in the noise-free scenario. #R##N#We propose an alternating minimization algorithm called sparse power factorization (SPF) for compressed sensing of sparse rank-one matrices. For a class of signals whose sparse representation coefficients are fast-decaying, SPF achieves stable recovery of the rank-1 matrix formed by their outer product and requires number of measurements within a logarithmic factor of the information-theoretic fundamental limit. For the recovery of general sparse low-rank matrices, we propose subspace-concatenated SPF (SCSPF), which has analogous near optimal performance guarantees to SPF in the rank-1 case. Numerical results show that SPF and SCSPF empirically outperform convex programmings using the best known combinations of mixed norm and nuclear norm.

Includes members:
  Member "rip like properties in subsampled blind deconvolution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the non convex geometry of low rank matrix optimization")
  Member "blind recovery of sparse signals from subsampled convolution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "phase retrieval of low rank matrices by anchored regression")
  Member "near optimal compressed sensing of sparse rank one matrices via sparse power factorization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the non convex geometry of low rank matrix optimization")
  Member "simultaneously structured models with application to sparse and low rank matrices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint activity detection and channel estimation for mmw thz wideband massive access")
  Member "blind deconvolution using convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")
  Member "oblique pursuits for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparse signal recovery via generalized entropy functions minimization")
  Member "optimal phase transitions in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimal errors and phase transitions in high dimensional generalized linear models")
  Member "blind compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an algebraic geometric approach for linear regression without correspondences")

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)