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 "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.

Includes members:
  Member "sketching for simultaneously sparse and low rank covariance matrices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal sample complexity for stable matrix recovery")
  Member "isometric sketching of any set via the restricted isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "quantized compressive sensing with rip matrices the benefit of dithering")
  Member "dimensionality reduction with subgaussian matrices a unified theory":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sub gaussian matrices on sets optimal tail dependence and applications")
  Member "fundamental performance limits for ideal decoders in high dimensional linear inverse problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the basins of attraction of the global minimizers of the non convex sparse spikes estimation problem")
  Member "compressed subspace matching on the continuum":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "phase retrieval of low rank matrices by anchored regression")
  Member "low rank matrix recovery from rank one measurements":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the convex geometry of blind deconvolution and matrix completion")
  Member "uniform recovery of fusion frame structured sparse signals":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the importance of phase in complex compressive sensing")
  Member "exact and stable covariance estimation from quadratic sampling via convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "phase retrieval of low rank matrices by anchored regression")

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)