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: "shannon theoretic limits on noisy compressive sampling"
  Abstract: In this paper, we study the number of measurements required to recover a sparse signal in ${\mathbb C}^M$ with $L$ non-zero coefficients from compressed samples in the presence of noise. For a number of different recovery criteria, we prove that $O(L)$ (an asymptotically linear multiple of $L$) measurements are necessary and sufficient if $L$ grows linearly as a function of $M$. This improves on the existing literature that is mostly focused on variants of a specific recovery algorithm based on convex programming, for which $O(L\log(M-L))$ measurements are required. We also show that $O(L\log(M-L))$ measurements are required in the sublinear regime ($L = o(M)$).

Connected through hyperedge HE-1 (induced by source paper "information theoretic lower bounds for compressive sensing with generative models"):
  with members:
    - "an introductory guide to fano s inequality with applications in statistical estimation"
    - "lower bounds for compressed sensing with generative models"
    - "solving inverse problems via auto encoders"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "the universal approximation power of finite width deep relu networks"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "benefits of depth in neural networks"
    - "understanding deep neural networks with rectified linear units"

Connected through hyperedge HE-2 (induced by source paper "learning mixtures of sparse linear regressions using sparse graph codes"):
  with members:
    - "solving a mixture of many random linear equations by tensor decomposition and alternating minimization"
    - "nonadaptive group testing with random set of defectives"
    - "fast and robust compressive phase retrieval with sparse graph codes"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "phasecode fast and efficient compressive phase retrieval based on sparse graph codes"
    - "sub linear time support recovery for compressed sensing using sparse graph codes"
    - "guess who rated this movie identifying users through subspace clustering"
    - "learning mixtures of linear classifiers"

Connected through hyperedge HE-3 (induced by source paper "stable support recovery of stream of pulses with application to ultrasound imaging"):
  with members:
    - "recovery of sparse positive signals on the sphere from low resolution measurements"
    - "super resolution on the sphere using convex optimization"
    - "sparse spikes deconvolution on thin grids"
    - "robust recovery of positive stream of pulses"
    - "robust recovery of stream of pulses using convex optimization"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "exact recovery of dirac ensembles from the projection onto spaces of spherical harmonics"
    - "exact recovery of non uniform splines from the projection onto spaces of algebraic polynomials"

Connected through hyperedge HE-4 (induced by source paper "capacity of gaussian many access channels"):
  with members:
    - "on or many access channels"
    - "sparse ofdm a compressive sensing approach to asynchronous neighbor discovery"
    - "identifying randomly activated users via sign compute resolve on graphs"
    - "coded slotted aloha a graph based method for uncoordinated multiple access"
    - "many access for small packets based on precoding and sparsity aware recovery"
    - "many access channels the gaussian case with random user activities"
    - "many broadcast channels definition and capacity in the degraded case"
    - "sparse signal processing with linear and non linear observations a unified shannon theoretic approach"

Connected through hyperedge HE-5 (induced by source paper "an information theoretic study for noisy compressed sensing with joint sparsity model 2"):
  with members:
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "subspace recovery from structured union of subspaces"
    - "measurement bounds for sparse signal ensembles via graphical models"
    - "support recovery of sparse signals in the presence of multiple measurement vectors"
    - "information theoretic bounds for compressed sensing"
    - "distributed compressive sensing"
    - "performance analysis for sparse support recovery"
    - "necessary and sufficient conditions for sparsity pattern recovery"

Connected through hyperedge HE-6 (induced by source paper "limits on support recovery with probabilistic models an information theoretic framework"):
  with members:
    - "converse bounds for noisy group testing with arbitrary measurement matrices"
    - "asymptotics of fingerprinting and group testing tight bounds from channel capacities"
    - "signal estimation with additive error metrics in compressed sensing"
    - "on the dispersions of three network information theory problems"
    - "group testing algorithms bounds and simulations"
    - "living on the edge phase transitions in convex programs with random data"
    - "sparse signal processing with linear and non linear observations a unified shannon theoretic approach"
    - "the capacity of adaptive group testing"

Connected through hyperedge HE-7 (induced by source paper "limits on support recovery with probabilistic models an information spectrum approach"):
  with members:
    - "on the dispersions of three network information theory problems"
    - "group testing algorithms bounds and simulations"
    - "living on the edge phase transitions in convex programs with random data"
    - "the capacity of adaptive group testing"
    - "support recovery with sparsely sampled free random matrices"
    - "boolean compressed sensing and noisy group testing"
    - "non adaptive probabilistic group testing with noisy measurements near optimal bounds with efficient algorithms"
    - "optimal phase transitions in compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "bayesian hypothesis test using nonparametric belief propagation for noisy sparse recovery"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "bayesian hypothesis test for sparse support recovery using belief propagation"
    - "necessary and sufficient conditions for sparsity pattern recovery"
    - "message passing algorithms for compressed sensing"
    - "bayesian compressive sensing via belief propagation"
    - "randomly spread cdma asymptotics via statistical physics"

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)