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 "a non convex blind calibration method for randomised sensing strategies"):
  Abstract: The implementation of computational sensing strategies often faces calibration problems typically solved by means of multiple, accurately chosen training signals, an approach that can be resource-consuming and cumbersome. Conversely, blind calibration does not require any training, but corresponds to a bilinear inverse problem whose algorithmic solution is an open issue. We here address blind calibration as a non-convex problem for linear random sensing models, in which we aim to recover an unknown signal from its projections on sub-Gaussian random vectors each subject to an unknown multiplicative factor (gain). To solve this optimisation problem we resort to projected gradient descent starting from a suitable initialisation. An analysis of this algorithm allows us to show that it converges to the global optimum provided a sample complexity requirement is met, i.e., relating convergence to the amount of information collected during the sensing process. Finally, we present some numerical experiments in which our algorithm allows for a simple solution to blind calibration of sensor gains in computational sensing applications.

Includes members:
  Member "through the haze a non convex approach to blind gain calibration for linear random sensing models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the basins of attraction of the global minimizers of the non convex sparse spikes estimation problem")
  Member "a geometric analysis of phase retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the convex geometry of blind deconvolution and matrix completion")
  Member "self calibration and biconvex compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")
  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 "phase retrieval via wirtinger flow theory and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "convex optimization approaches for blind sensor calibration using sparsity":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "bilinear recovery using adaptive vector amp")

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)