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: "compressed sensing over ell_p balls minimax mean square error"
  Abstract: We consider the compressed sensing problem, where the object $x_0 \in \bR^N$ is to be recovered from incomplete measurements $y = Ax_0 + z$; here the sensing matrix $A$ is an $n \times N$ random matrix with iid Gaussian entries and $n < N$. A popular method of sparsity-promoting reconstruction is $\ell^1$-penalized least-squares reconstruction (aka LASSO, Basis Pursuit). #R##N#It is currently popular to consider the strict sparsity model, where the object $x_0$ is nonzero in only a small fraction of entries. In this paper, we instead consider the much more broadly applicable $\ell_p$-sparsity model, where $x_0$ is sparse in the sense of having $\ell_p$ norm bounded by $\xi \cdot N^{1/p}$ for some fixed $0   0$. #R##N#We study an asymptotic regime in which $n$ and $N$ both tend to infinity with limiting ratio $n/N = \delta \in (0,1)$, both in the noisy ($z \neq 0$) and noiseless ($z=0$) cases. Under weak assumptions on $x_0$, we are able to precisely evaluate the worst-case asymptotic minimax mean-squared reconstruction error (AMSE) for $\ell^1$ penalized least-squares: min over penalization parameters, max over $\ell_p$-sparse objects $x_0$. We exhibit the asymptotically least-favorable object (hardest sparse signal to recover) and the maximin penalization. #R##N#Our explicit formulas unexpectedly involve quantities appearing classically in statistical decision theory. Occurring in the present setting, they reflect a deeper connection between penalized $\ell^1$ minimization and scalar soft thresholding. This connection, which follows from earlier work of the authors and collaborators on the AMP iterative thresholding algorithm, is carefully explained. #R##N#Our approach also gives precise results under weak-$\ell_p$ ball coefficient constraints, as we show here.

Connected through hyperedge HE-1 (induced by source paper "simple bounds for noisy linear inverse problems with exact side information"):
  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 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"
    - "estimation of simultaneously sparse and low rank matrices"
    - "compressive principal component pursuit"

Connected through hyperedge HE-2 (induced by source paper "approximate message passing with consistent parameter estimation and applications to sparse learning"):
  with members:
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "expectation maximization gaussian mixture approximate message passing"
    - "message passing de quantization with applications to compressed sensing"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "iterative estimation of constrained rank one matrices in noise"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-3 (induced by source paper "minimum complexity pursuit for universal compressed sensing"):
  with members:
    - "an mcmc approach to universal lossy compression of analog sources"
    - "signal recovery on incoherent manifolds"
    - "recovery from linear measurements with complexity matching universal signal estimation"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "sampling and recovery of pulse streams"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "model based compressive sensing"
    - "optimally tuned iterative reconstruction algorithms for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "asymptotic analysis of complex lasso via complex approximate message passing camp"):
  with members:
    - "expectation maximization gaussian mixture approximate message passing"
    - "on phase transition of compressed sensing in the complex domain"
    - "approximate message passing with consistent parameter estimation and applications to sparse learning"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "a message passing receiver for bicm ofdm over unknown clustered sparse channels"
    - "minimum complexity pursuit"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "compressive imaging using approximate message passing and a markov tree prior"

Connected through hyperedge HE-5 (induced by source paper "a power efficient sensing communication scheme joint source channel network coding by using compressive sensing"):
  with members:
    - "compressive sensing over networks"

Connected through hyperedge HE-6 (induced by source paper "how well can we estimate a sparse vector"):
  with members:
    - "information theoretic bounds for compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "generalized approximate message passing for estimation with random linear mixing"):
  with members:
    - "message passing de quantization with applications to compressed sensing"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "a message passing receiver for bicm ofdm over unknown clustered sparse channels"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "compressive imaging using approximate message passing and a markov tree prior"
    - "belief propagation methods for intercell interference coordination"
    - "estimation with random linear mixing belief propagation and compressed sensing"
    - "graphical models concepts in compressed sensing"

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)