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 "robust recovery of positive stream of pulses"):
  Abstract: The problem of estimating the delays and amplitudes of a positive stream of pulses appears in many applications, such as single-molecule microscopy. This paper suggests estimating the delays and amplitudes using a convex program, which is robust in the presence of noise (or model mismatch). Particularly, the recovery error is proportional to the noise level. We further show that the error grows exponentially with the density of the delays and also depends on the localization properties of the pulse.

Includes members:
  Member "compressive spectral estimation with single snapshot esprit stability and resolution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "super resolution limit of the esprit algorithm")
  Member "stable support recovery of stream of pulses with application to ultrasound imaging":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stable super resolution of images a theoretical study")
  Member "music for multidimensional spectral estimation stability and super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "conditioning of restricted fourier matrices and super resolution of music")
  Member "recovery of sparse positive signals on the sphere from low resolution measurements":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the geometry of off the grid compressed sensing")
  Member "super resolution of positive sources the discrete setup":
    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 "support recovery for sparse deconvolution of positive measures":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adapting to unknown noise level in sparse deconvolution")
  Member "super resolution on the sphere using convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "blind two dimensional super resolution and its performance guarantee")
  Member "sparse spikes deconvolution on thin grids":
    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)