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: "a performance analysis framework for socp algorithms in noisy compressed sensing"
  Abstract: Solving under-determined systems of linear equations with sparse solutions attracted enormous amount of attention in recent years, above all, due to work of \cite{CRT,CanRomTao06,DonohoPol}. In \cite{CRT,CanRomTao06,DonohoPol} it was rigorously shown for the first time that in a statistical and large dimensional context a linear sparsity can be recovered from an under-determined system via a simple polynomial $\ell_1$-optimization algorithm. \cite{CanRomTao06} went even further and established that in \emph{noisy} systems for any linear level of under-determinedness there is again a linear sparsity that can be \emph{approximately} recovered through an SOCP (second order cone programming) noisy equivalent to $\ell_1$. Moreover, the approximate solution is (in an $\ell_2$-norm sense) guaranteed to be no further from the sparse unknown vector than a constant times the noise. In this paper we will also consider solving \emph{noisy} linear systems and present an alternative statistical framework that can be used for their analysis. To demonstrate how the framework works we will show how one can use it to precisely characterize the approximation error of a wide class of SOCP algorithms. We will also show that our theoretical predictions are in a solid agrement with the results one can get through numerical simulations.

Connected through hyperedge HE-1 (induced by source paper "sharp time data tradeoffs for linear inverse problems"):
  with members:
    - "isometric sketching of any set via the restricted isometry property"
    - "the generalized lasso with non linear observations"
    - "does ell_p minimization outperform ell_1 minimization"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "learning without concentration"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "from denoising to compressed sensing"
    - "simple bounds for noisy linear inverse problems with exact side information"

Connected through hyperedge HE-2 (induced by source paper "sharp mse bounds for proximal denoising"):
  with members:
    - "corrupted sensing novel guarantees for separating structured signals"
    - "living on the edge phase transitions in convex programs with random data"
    - "a framework to characterize performance of lasso algorithms"
    - "the phase transition of matrix recovery from gaussian measurements matches the minimax mse of matrix denoising"
    - "the squared error of generalized lasso a precise analysis"
    - "guarantees of total variation minimization for signal recovery"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "atomic norm denoising with applications to line spectral estimation"

Connected through hyperedge HE-3 (induced by source paper "the squared error of generalized lasso a precise analysis"):
  with members:
    - "corrupted sensing novel guarantees for separating structured signals"
    - "the achievable performance of convex demixing"
    - "living on the edge phase transitions in convex programs with random data"
    - "regularly random duality"
    - "a framework to characterize performance of lasso algorithms"
    - "the phase transition of matrix recovery from gaussian measurements matches the minimax mse of matrix denoising"
    - "sharp mse bounds for proximal denoising"
    - "a rigorous geometry probability equivalence in characterization of ell_1 optimization"

Connected through hyperedge HE-4 (induced by source paper "a problem dependent analysis of socp algorithms in noisy compressed sensing"):
  with members:
    - "model based compressive sensing"
    - "various thresholds for ell_1 optimization in compressed sensing"
    - "block length dependent thresholds in block sparse compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "weighted superimposed codes and constrained integer compressed sensing"
    - "algorithmic linear dimension reduction in the l_1 norm for sparse vectors"

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)