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: "precise error analysis of regularized m estimators in high dimensions"
  Abstract: A popular approach for estimating an unknown signal from noisy, linear measurements is via solving a so called \emph{regularized M-estimator}, which minimizes a weighted combination of a convex loss function and of a convex (typically, non-smooth) regularizer. We accurately predict the squared error performance of such estimators in the high-dimensional proportional regime. The random measurement matrix is assumed to have entries iid Gaussian, only minimal and rather mild regularity conditions are imposed on the loss function, the regularizer, and on the noise and signal distributions. We show that the error converges in probability to a nontrivial limit that is given as the solution to a minimax convex-concave optimization problem on four scalar optimization variables. We identify a new summary parameter, termed the Expected Moreau envelope to play a central role in the error characterization. The \emph{precise} nature of the results permits an accurate performance comparison between different instances of regularized M-estimators and allows to optimally tune the involved parameters (e.g. regularizer parameter, number of measurements). The key ingredient of our proof is the \emph{Convex Gaussian Min-max Theorem} (CGMT) which is a tight and strengthened version of a classical Gaussian comparison inequality that was proved by Gordon in 1988.

Connected through hyperedge HE-1 (induced by source paper "precise performance analysis of the box elastic net under matrix uncertainties"):
  with members:
    - "a joint indoor wlan localization and outlier detection scheme using lasso and elastic net optimization techniques"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "asymptotics and optimal designs of slope for sparse linear regression"):
  with members:
    - "decreasing weighted sorted ell_1 regularization"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "optimal data detection in large mimo"):
  with members:
    - "on the performance of mismatched data detection in large mimo systems"
    - "ber analysis of the box relaxation for bpsk signal recovery"
    - "compressive phase retrieval via generalized approximate message passing"
    - "does ell_p minimization outperform ell_1 minimization"
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "from denoising to compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "optimization based amp for phase retrieval the impact of initialization and ell_2 regularization"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "a precise analysis of phasemax in phase retrieval"
    - "phase retrieval via linear programming fundamental limits and algorithmic improvements"
    - "coordinate descent algorithms for phase retrieval"
    - "fundamental limits of phasemax for phase retrieval a replica analysis"
    - "phase transitions of spectral initialization for high dimensional nonconvex estimation"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"

Connected through hyperedge HE-5 (induced by source paper "a precise analysis of phasemax in phase retrieval"):
  with members:
    - "phase retrieval via linear programming fundamental limits and algorithmic improvements"
    - "fundamental limits of phasemax for phase retrieval a replica analysis"
    - "multiple illumination phaseless super resolution mips with applications to phaseless doa estimation and diffraction imaging"
    - "blind deconvolution with additional autocorrelations via convex programs"
    - "phasemax convex phase retrieval via basis pursuit"
    - "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"
    - "ber analysis of the box relaxation for bpsk signal recovery"

Connected through hyperedge HE-6 (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"
    - "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 an overview of recent developments"

Connected through hyperedge HE-7 (induced by source paper "precise performance analysis of the lasso under matrix uncertainties"):
  with members:
    - "a framework to characterize performance of lasso algorithms"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-8 (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"
    - "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"
    - "a field guide to forward backward splitting with a fasta implementation"

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)