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 "exact dimensionality reduction for partial line spectra estimation problems"):
  Abstract: Line spectral estimation theory aims to estimate the off-the-grid spectral components of a time signal with optimal precision. Recent results have shown that it is possible to recover signals having sparse line spectra from few temporal observations via the use of convex programming. However, the computational cost of such approaches remains the major flaw to their application to practical systems. This work investigates the recovery of spectrally sparse signal from low-dimensional partial measurements. It is shown in the first part of this paper that, under a light assumption on the sub-sampling matrix, the partial line spectral estimation problems can be relaxed into a low-dimensional semidefinite program. The proof technique relies on a novel extension of the Gram parametrization to subspaces of trigonometric polynomials. #R##N#The second part of this work focuses on the analysis of two particular sub-sampling patterns: multirate sampling and random selection sampling. It is shown that those sampling patterns guarantee perfect recovery of the line spectra, and that the reconstruction can be achieved in a poly-logarithmic time with respect to the full observation case. Moreover, the sub-Nyquist recovery capabilities of such sampling patterns are highlighted. The atomic soft thresholding method is adapted in the presented framework to estimate sparse spectra in noisy environments, and a scalable algorithm for its resolution is proposed.

Includes members:
  Member "super resolution of complex exponentials from modulations with unknown waveforms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "resolution limit for line spectral estimation theory and algorithm")
  Member "robust recovery of complex exponential signals from random gaussian projections via low rank hankel matrix reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "atomic norm denoising for complex exponentials with unknown waveform modulations")
  Member "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi dimensional spectral super resolution with prior knowledge via frequency selective vandermonde decomposition and admm")
  Member "robust recovery of stream of pulses using convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the stable resolution limit of total variation regularization for spike deconvolution")
  Member "off the grid line spectrum denoising and estimation with multiple measurement vectors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "exact joint sparse frequency recovery via optimization methods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "super resolution radar":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "tree search network for sparse regression")
  Member "near minimax line spectral estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")

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)