human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "identifiability in blind deconvolution under minimal assumptions"
  Abstract: Blind deconvolution (BD) arises in many applications. Without assumptions on the signal and the filter, BD is ill-posed. In practice, subspace or sparsity assumptions have shown the ability to reduce the search space and yield the unique solution. However, existing theoretical analysis on uniqueness in BD is rather limited. In an earlier paper of ours, we provided the first algebraic sample complexities for BD with almost all bases or frames. We showed that for BD of vectors in $\mathbb{C}^n$, with two subspace constraints of dimensions $m_1$ and $m_2$, the required sample complexity is $n\geq m_1m_2$. This result is suboptimal, since the number of degrees of freedom is merely $m_1+m_2-1$. We provided analogus results, with similar suboptimality, for BD with sparsity or mixed subspace and sparsity constraints. In this paper, taking advantage of the recent progress on the information-theoretic limits of low-rank matrix recovery, we are finally able to bridge this gap, and derive an optimal sample complexity result for BD with generic bases or frames. We show that for BD of vectors in $\mathbb{C}^n$, with two subspace constraints of dimensions $m_1$ and $m_2$, the required sample complexity is $n > m_1+m_2$. We present the extensions of this result to BD with sparsity constraints or mixed constraints, with the sparsity level replacing the subspace dimension.

Connected through hyperedge HE-1 (induced by source paper "complete dictionary recovery over the sphere i overview and the geometric picture"):
  with members:
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "polynomial time tensor decompositions with sum of squares"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"
    - "a geometric analysis of phase retrieval"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "simple efficient and neural algorithms for sparse coding"

Connected through hyperedge HE-2 (induced by source paper "parametric bilinear generalized approximate message passing"):
  with members:
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "self calibration and biconvex compressive sensing"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "compressive phase retrieval via generalized approximate message passing"
    - "identifiability and stability in blind deconvolution under minimal assumptions"
    - "identifiability in blind deconvolution with subspace or sparsity constraints"
    - "fundamental limits of blind deconvolution part ii sparsity ambiguity trade offs"
    - "convex optimization approaches for blind sensor calibration using sparsity"

Connected through hyperedge HE-3 (induced by source paper "blind recovery of sparse signals from subsampled convolution"):
  with members:
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "self calibration and biconvex compressive sensing"
    - "rip like properties in subsampled blind deconvolution"
    - "lifting for blind deconvolution in random mask imaging identifiability and convex relaxation"
    - "identifiability and stability in blind deconvolution under minimal assumptions"
    - "a unified framework for identifiability analysis in bilinear inverse problems with applications to subspace and sparsity models"
    - "identifiability in blind deconvolution with subspace or sparsity constraints"
    - "identifiability scaling laws in bilinear inverse problems"

Question: Please tell me which class the center node 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)