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: "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.

Connected through hyperedge HE-1 (induced by source paper "low dimensional atomic norm representations in line spectral estimation"):
  with members:
    - "super resolution of complex exponentials from modulations with unknown waveforms"
    - "achieving super resolution in multi rate sampling systems via efficient semidefinite programming"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "near minimax line spectral estimation"
    - "atomic norm denoising with applications to line spectral estimation"
    - "compressed sensing off the grid"
    - "super resolution from noisy data"

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)