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 "blind deconvolution with additional autocorrelations via convex programs"):
  Abstract: In this work we characterize all ambiguities of the linear (aperiodic) one-dimensional convolution on two fixed finite-dimensional complex vector spaces. It will be shown that the convolution ambiguities can be mapped one-to-one to factorization ambiguities in the $z-$domain, which are generated by swapping the zeros of the input signals. We use this polynomial description to show a deterministic version of a recently introduced masked Fourier phase retrieval design. In the noise-free case a (convex) semi-definite program can be used to recover exactly the input signals if they share no common factors (zeros). Then, we reformulate the problem as deterministic blind deconvolution with prior knowledge of the autocorrelations. Numerically simulations show that our approach is also robust against additive noise.

Includes members:
  Member "reconstruction of signals from their autocorrelation and cross correlation vectors with applications to phase retrieval and blind channel estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "noncoherent short packet communication via modulation on conjugated zeros")
  Member "stft phase retrieval uniqueness guarantees and recovery algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "toward a mathematical theory of the crystallographic phase retrieval problem")
  Member "on the stability of sparse convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stable blind deconvolution over the reals from additional autocorrelations")
  Member "fundamental limits of blind deconvolution part i ambiguity kernel":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "blind phaseless short time fourier transform recovery")
  Member "stable recovery from the magnitude of symmetrized fourier measurements":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sparse model uncertainties in compressed sensing with application to convolutions and sporadic communication")
  Member "compressed sensing on the image of bilinear maps":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparse model uncertainties in compressed sensing with application to convolutions and sporadic communication")

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)