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 framework to characterize performance of lasso algorithms"
  Abstract: In this paper we consider solving \emph{noisy} under-determined systems of linear equations with sparse solutions. A noiseless equivalent attracted enormous attention in recent years, above all, due to work of \cite{CRT,CanRomTao06,DonohoPol} where it was shown in a statistical and large dimensional context that a sparse unknown vector (of sparsity proportional to the length of the vector) can be recovered from an under-determined system via a simple polynomial $\ell_1$-optimization algorithm. \cite{CanRomTao06} further established that even when the equations are \emph{noisy}, one can, through an SOCP noisy equivalent of $\ell_1$, obtain an approximate solution that is (in an $\ell_2$-norm sense) no further than a constant times the noise from the sparse unknown vector. In our recent works \cite{StojnicCSetam09,StojnicUpper10}, we created a powerful mechanism that helped us characterize exactly the performance of $\ell_1$ optimization in the noiseless case (as shown in \cite{StojnicEquiv10} and as it must be if the axioms of mathematics are well set, the results of \cite{StojnicCSetam09,StojnicUpper10} are in an absolute agreement with the corresponding exact ones from \cite{DonohoPol}). In this paper we design a mechanism, as powerful as those from \cite{StojnicCSetam09,StojnicUpper10}, that can handle the analysis of a LASSO type of algorithm (and many others) that can be (or typically are) used for "solving" noisy under-determined systems. Using the mechanism we then, in a statistical context, compute the exact worst-case $\ell_2$ norm distance between the unknown sparse vector and the approximate one obtained through such a LASSO. The obtained results match the corresponding exact ones obtained in \cite{BayMon10,DonMalMon10}. Moreover, as a by-product of our analysis framework we recognize existence of an SOCP type of algorithm that achieves the same performance.

Connected through hyperedge HE-1 (induced by source paper "a simple bound on the ber of the map decoder for massive mimo systems"):
  with members:
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "the mutual information in random linear estimation"
    - "optimality of large mimo detection via approximate message passing"
    - "massive mimo for next generation wireless systems"
    - "energy and spectral efficiency of very large multiuser mimo systems"
    - "randomly spread cdma asymptotics via statistical physics"

Connected through hyperedge HE-2 (induced by source paper "phase retrieval via polytope optimization geometry phase transitions and new algorithms"):
  with members:
    - "a precise analysis of phasemax in phase retrieval"
    - "phase retrieval via linear programming fundamental limits and algorithmic improvements"
    - "fundamental limits of phasemax for phase retrieval a replica analysis"
    - "phase transitions of spectral initialization for high dimensional nonconvex estimation"
    - "precise error analysis of regularized m estimators in high dimensions"
    - "an elementary proof of convex phase retrieval in the natural parameter space via the linear program phasemax"
    - "phase retrieval meets statistical learning theory a flexible convex relaxation"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"

Connected through hyperedge HE-3 (induced by source paper "the generalized lasso for sub gaussian measurements with dithered quantization"):
  with members:
    - "quantized compressive sensing with rip matrices the benefit of dithering"
    - "learning compact neural networks with regularization"
    - "non gaussian hyperplane tessellations and robust one bit compressed sensing"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "learning without concentration"
    - "living on the edge phase transitions in convex programs with random data"
    - "the squared error of generalized lasso a precise analysis"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-4 (induced by source paper "precise performance analysis of the lasso under matrix uncertainties"):
  with members:
    - "precise error analysis of regularized m estimators in high dimensions"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "phase retrieval via linear programming fundamental limits and algorithmic improvements"):
  with members:
    - "fundamental limits of phasemax for phase retrieval a replica analysis"
    - "phase transitions of spectral initialization for high dimensional nonconvex estimation"
    - "phasemax convex phase retrieval via basis pursuit"
    - "precise error analysis of regularized m estimators in high dimensions"
    - "an elementary proof of convex phase retrieval in the natural parameter space via the linear program phasemax"
    - "phase retrieval meets statistical learning theory a flexible convex relaxation"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval via wirtinger flow theory and algorithms"

Connected through hyperedge HE-6 (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-7 (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-8 (induced by source paper "robust recovery of complex exponential signals from random gaussian projections via low rank hankel matrix reconstruction"):
  with members:
    - "robust spectral compressed sensing via structured matrix completion"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "super resolution extremal functions and the condition number of vandermonde matrices"
    - "the squared error of generalized lasso a precise analysis"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "the power of convex relaxation near optimal matrix completion"

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)