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 "the basins of attraction of the global minimizers of the non convex sparse spikes estimation problem"):
  Abstract: The sparse spike estimation problem consists in estimating a number of off-the-grid impulsive sources from under-determined linear measurements. Information theoretic results ensure that the minimization of a non-convex functional is able to recover the spikes for adequatly chosen measurements (deterministic or random). To solve this problem, methods inspired from the case of finite dimensional sparse estimation where a convex program is used have been proposed. Also greedy heuristics have shown nice practical results. However, little is known on the ideal non-convex minimization to perform. In this article, we study the shape of the global minimum of this non-convex functional: we give an explicit basin of attraction of the global minimum that shows that the non-convex problem becomes easier as the number of measurements grows. This has important consequences for methods involving descent algorithms (such as the greedy heuristic) and it gives insights for potential improvements of such descent methods.

Includes members:
  Member "support localization and the fisher metric for off the grid sparse regularization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the geometry of off the grid compressed sensing")
  Member "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "blind over the air computation and data fusion via provable wirtinger flow")
  Member "inexact gradient projection and fast data driven compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "coverblip accelerated and scalable iterative matched filtering for magnetic resonance fingerprint reconstruction")
  Member "sketching for large scale learning of mixture models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "through the haze a non convex approach to blind gain calibration for linear random sensing models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sparse blind deconvolution for distributed radar autofocus imaging")
  Member "compressive k means":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "adapting to unknown noise level in sparse deconvolution":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "data driven estimation of sinusoid frequencies")
  Member "exact solutions to super resolution on semi algebraic domains in higher dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "frequency selective vandermonde decomposition of toeplitz matrices with applications")

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)