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 "from blind deconvolution to blind super resolution through convex programming"):
  Abstract: This paper discusses the recovery of an unknown signal $x\in \mathbb{R}^L$ through the result of its convolution with an unknown filter $h \in \mathbb{R}^L$. This problem, also known as blind deconvolution, has been studied extensively by the signal processing and applied mathematics communities, leading to a diversity of proofs and algorithms based on various assumptions on the filter and its input. Sparsity of this filter, or in contrast, non vanishing of its Fourier transform are instances of such assumptions. The main result of this paper shows that blind deconvolution can be solved through nuclear norm relaxation in the case of a fully unknown channel, as soon as this channel is probed through a few $N \gtrsim \mu^2_m K^{1/2}$ input signals $x_n = C_n m_n$, $n=1,\ldots,N,$ that are living in known $K$-dimensional subspaces $C_n$ of $\mathbb{R}^L$. This result holds with high probability on the genericity of the subspaces $C_n$ as soon as $L\gtrsim K^{3/2}$ and $N\gtrsim K^{1/2}$ up to log factors. Our proof system relies on the construction of a certificate of optimality for the underlying convex program. This certificate expands as a Neumann series and is shown to satisfy the conditions for the recovery of the matrix encoding the unknowns by controlling the terms in this series. An incidental consequence of the result of this paper, following from the lack of assumptions on the filter, is that nuclear norm relaxation can be extended from blind deconvolution to blind super-resolution, as soon as the unknown ideal low pass filter has a sufficiently large support compared to the ambient dimension $L$. Numerical experiments supporting the theory as well as its application to blind super-resolution are provided.

Includes members:
  Member "a non convex blind calibration method for randomised sensing strategies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse blind deconvolution for distributed radar autofocus imaging")
  Member "self calibration and bilinear inverse problems via linear least squares":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")
  Member "rapid robust and reliable blind deconvolution via nonconvex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a deterministic convergence framework for exact non convex phase retrieval")
  Member "leveraging diversity and sparsity in blind deconvolution":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "phase retrieval of low rank matrices by anchored regression")
  Member "super resolution of complex exponentials from modulations with unknown waveforms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "resolution limit for line spectral estimation theory and algorithm")
  Member "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")
  Member "blind deconvolution meets blind demixing algorithms and performance bounds":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")

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)