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 "empirical chaos processes and blind deconvolution"):
  Abstract: This paper investigates conditions under which certain kinds of systems of bilinear equations have a unique structured solution. In particular, we look at when we can recover vectors $\boldsymbol{w},\boldsymbol{q}$ from observations of the form \[ y_{\ell} =   , \quad \ell = 1,\ldots,L, \] where $\boldsymbol{b}_\ell,\boldsymbol{c}_\ell$ are known. We show that if $\boldsymbol{w}\in\mathbb{C}^{M_1}$ and $\boldsymbol{q}\in\mathbb{C}^{M_2}$ are sparse, with no more than $K$ and $N$ nonzero entries, respectively, and the $\boldsymbol{b}_\ell,\boldsymbol{c}_\ell$ are generic, selected as independent Gaussian random vectors, then $\boldsymbol{w},\boldsymbol{q}$ are uniquely determined from \[ #R##N#L \geq \mathrm{Const}\cdot (K+N)\log^5(M_1M_2) \] such equations with high probability. #R##N#The key ingredient in our analysis is a uniform probabilistic bound on how far a random process of the form \[Z(\boldsymbol{X}) = \sum_{\ell=1}^L|\boldsymbol{b}_\ell^*\boldsymbol{X}\boldsymbol{c}_\ell|^2 \] deviates from its mean over a set of structured matrices $\boldsymbol{X}\in\mathcal{X}$. As both $\boldsymbol{b}_\ell$ and $\boldsymbol{c}_\ell$ are random, this is a specialized type of $4$th order chaos; we refer to $Z(\boldsymbol{X})$ as an {\em empirical chaos process}. Bounding this process yields a set of general conditions for when the map $\boldsymbol{X}\rightarrow \{\boldsymbol{b}_\ell^*\boldsymbol{X}\boldsymbol{c}_\ell\}_{\ell=1}^L$ is a restricted isometry over the set of matrices $\mathcal{X}$. The conditions are stated in terms of general geometric properties of the set $\mathcal{X}$, and are explicitly computed for the case where $\mathcal{X}$ is the set of matrices that are simultaneously sparse and low rank.

Includes members:
  Member "rapid robust and reliable blind deconvolution via nonconvex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a deterministic convergence framework for exact non convex phase retrieval")
  Member "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the convex geometry of blind deconvolution and matrix completion")
  Member "recipes for stable linear embeddings from hilbert spaces to ℝ m":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "functional nonlinear sparse models")
  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 "solving random quadratic systems of equations is nearly as easy as solving linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "rip like properties in subsampled blind deconvolution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the non convex geometry of low rank matrix optimization")
  Member "lifting for blind deconvolution in random mask imaging identifiability and convex relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "blind deconvolution using modulated inputs")
  Member "identifiability and stability in blind deconvolution under minimal assumptions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "blind phaseless short time fourier transform recovery")

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)