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 "super resolution of complex exponentials from modulations with unknown waveforms"):
  Abstract: Super-resolution is generally referred to as the task of recovering fine details from coarse information. Motivated by applications such as single-molecule imaging, radar imaging, etc., we consider parameter estimation of complex exponentials from their modulations with unknown waveforms, allowing for non-stationary blind super-resolution. This problem, however, is ill-posed since both the parameters associated with the complex exponentials and the modulating waveforms are unknown. To alleviate this, we assume that the unknown waveforms live in a common low-dimensional subspace. Using a lifting trick, we recast the blind super-resolution problem as a structured low-rank matrix recovery problem. Atomic norm minimization is then used to enforce the structured low-rankness, and is reformulated as a semidefinite program that is solvable in polynomial time. We show that, up to scaling ambiguities, exact recovery of both of the complex exponential parameters and the unknown waveforms is possible when the waveform subspace is random and the number of measurements is proportional to the number of degrees of freedom in the problem. Numerical simulations support our theoretical findings, showing that non-stationary blind super-resolution using atomic norm minimization is possible.

Includes members:
  Member "guaranteed blind sparse spikes deconvolution via lifting and convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")
  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 "self calibration and biconvex compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")
  Member "super resolution of positive sources the discrete setup":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "lifting for blind deconvolution in random mask imaging identifiability and convex relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "blind deconvolution using modulated inputs")
  Member "blind deconvolution meets blind demixing algorithms and performance bounds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")
  Member "identifiability in blind deconvolution with subspace or sparsity constraints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi target detection with application to cryo electron microscopy")
  Member "off the grid line spectrum denoising and estimation with multiple measurement vectors":
    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)