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: "recipes for stable linear embeddings from hilbert spaces to ℝ m"
  Abstract: We consider the problem of constructing a linear map from a Hilbert space $\mathcal{H}$ (possibly infinite dimensional) to $\mathbb{R}^m$ that satisfies a restricted isometry property (RIP) on an arbitrary signal model $\mathcal{S} \subset \mathcal{H}$. We present a generic framework that handles a large class of low-dimensional subsets but also unstructured and structured linear maps. We provide a simple recipe to prove that a random linear map satisfies a general RIP on $\mathcal{S}$ with high probability. We also describe a generic technique to construct linear maps that satisfy the RIP. Finally, we detail how to use our results in several examples, which allow us to recover and extend many known compressive sampling results.

Connected through hyperedge HE-1 (induced by source paper "functional nonlinear sparse models"):
  with members:
    - "learning power spectrum maps from quantized power measurements"
    - "block iterative reweighted algorithms for super resolution of spectrally sparse signals"
    - "forward backward greedy algorithms for atomic norm regularization"
    - "computational complexity of certifying restricted isometry property"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "atomic norm denoising with applications to line spectral estimation"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"

Connected through hyperedge HE-2 (induced by source paper "optimality of 1 norm regularization among weighted 1 norms for sparse recovery a case study on how to find optimal regularizations"):
  with members:
    - "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"
    - "living on the edge phase transitions in convex programs with random data"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"

Connected through hyperedge HE-3 (induced by source paper "compressed sensing in hilbert spaces"):
  with members:
    - "sketching for large scale learning of mixture models"
    - "compressive k means"
    - "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"
    - "uniform recovery of fusion frame structured sparse signals"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"

Connected through hyperedge HE-4 (induced by source paper "time for dithering fast and quantized random embeddings via the restricted isometry property"):
  with members:
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "near optimal sample complexity bounds for circulant binary embedding"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "self calibration and biconvex compressive sensing"
    - "isometric sketching of any set via the restricted isometry property"
    - "representation and coding of signal geometry"
    - "small width low distortions quantized random embeddings of low complexity sets"

Connected through hyperedge HE-5 (induced by source paper "empirical chaos processes and blind deconvolution"):
  with members:
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "self calibration and biconvex compressive sensing"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "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"
    - "blind recovery of sparse signals from subsampled convolution"

Connected through hyperedge HE-6 (induced by source paper "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"):
  with members:
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"
    - "uniform recovery of fusion frame structured sparse signals"
    - "living on the edge phase transitions in convex programs with random data"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "stable restoration and separation of approximately sparse signals"

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)