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: "upper bounding ell_1 optimization weak thresholds"
  Abstract: In our recent work \cite{StojnicCSetam09} we considered solving under-determined systems of linear equations with sparse solutions. In a large dimensional and statistical context we proved that if the number of equations in the system is proportional to the length of the unknown vector then there is a sparsity (number of non-zero elements of the unknown vector) also proportional to the length of the unknown vector such that a polynomial $\ell_1$-optimization technique succeeds in solving the system. We provided lower bounds on the proportionality constants that are in a solid numerical agreement with what one can observe through numerical experiments. Here we create a mechanism that can be used to derive the upper bounds on the proportionality constants. Moreover, the upper bounds obtained through such a mechanism match the lower bounds from \cite{StojnicCSetam09} and ultimately make the latter ones optimal.

Connected through hyperedge HE-1 (induced by source paper "precise error analysis of regularized m estimators in high dimensions"):
  with members:
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "ber analysis of the box relaxation for bpsk signal recovery"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "isotropically random orthogonal matrices performance of lasso and minimum conic singular values"
    - "the generalized lasso with non linear observations"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "statistical estimation from denoising to sparse regression and hidden cliques"
    - "convexity in source separation models geometry and algorithms"

Connected through hyperedge HE-2 (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-3 (induced by source paper "the gaussian min max theorem in the presence of convexity"):
  with members:
    - "convex recovery of a structured signal from independent random linear measurements"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "from denoising to compressed sensing"
    - "simple bounds for noisy linear inverse problems with exact side information"
    - "living on the edge phase transitions in convex programs with random data"
    - "a framework to characterize performance of lasso algorithms"
    - "the squared error of generalized lasso a precise analysis"
    - "meshes that trap random subspaces"

Connected through hyperedge HE-4 (induced by source paper "a framework to characterize performance of lasso algorithms"):
  with members:
    - "a rigorous geometry probability equivalence in characterization of ell_1 optimization"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "model based compressive sensing"
    - "various thresholds for ell_1 optimization in compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "dense error correction via l1 minimization"
    - "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)