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: "recovery from linear measurements with complexity matching universal signal estimation"
  Abstract: We study the compressed sensing (CS) signal estimation problem where an input signal is measured via a linear matrix multiplication under additive noise. While this setup usually assumes sparsity or compressibility in the input signal during recovery, the signal structure that can be leveraged is often not known a priori. In this paper, we consider universal CS recovery, where the statistics of a stationary ergodic signal source are estimated simultaneously with the signal itself. Inspired by Kolmogorov complexity and minimum description length, we focus on a maximum a posteriori (MAP) estimation framework that leverages universal priors to match the complexity of the source. Our framework can also be applied to general linear inverse problems where more measurements than in CS might be needed. We provide theoretical results that support the algorithmic feasibility of universal MAP estimation using a Markov chain Monte Carlo implementation, which is computationally challenging. We incorporate some techniques to accelerate the algorithm while providing comparable and in many cases better reconstruction quality than existing algorithms. Experimental results show the promise of universality in CS, particularly for low-complexity sources that do not exhibit standard sparsity or compressibility.

Connected through hyperedge HE-1 (induced by source paper "nonlinear function estimation with empirical bayes and approximate message passing"):
  with members:
    - "approximate message passing algorithm with universal denoising and gaussian mixture learning"
    - "empirical bayes and full bayes for signal estimation"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "sparse volterra and polynomial regression models recoverability and estimation"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "graphical models concepts in compressed sensing"
    - "message passing algorithms for compressed sensing"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-2 (induced by source paper "theoretical links between universal and bayesian compressed sensing algorithms"):
  with members:
    - "an efficient algorithm for compression based compressed sensing"
    - "on the information dimension of stochastic processes"
    - "compression based compressed sensing"
    - "new approach to bayesian high dimensional linear regression"
    - "from denoising to compressed sensing"
    - "minimum complexity pursuit for universal compressed sensing"
    - "lossy compression of discrete sources via the viterbi algorithm"

Connected through hyperedge HE-3 (induced by source paper "statistical physics and information theory perspectives on linear inverse problems"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "approximate message passing algorithm with universal denoising and gaussian mixture learning"
    - "performance trade offs in multi processor approximate message passing"
    - "multi processor approximate message passing using lossy compression"
    - "phase transitions in sparse pca"
    - "compressive imaging via approximate message passing with image denoising"
    - "improving m sbl for joint sparse recovery using a subspace penalty"
    - "mismatched estimation in large linear systems"

Connected through hyperedge HE-4 (induced by source paper "approximate message passing with universal denoising"):
  with members:
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "compressive imaging via approximate message passing with image denoising"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "mismatched estimation in large linear systems"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "signal estimation with additive error metrics in compressed sensing"
    - "from denoising to compressed sensing"
    - "sparse estimation with the swept approximated message passing algorithm"

Connected through hyperedge HE-5 (induced by source paper "approximate message passing algorithm with universal denoising and gaussian mixture learning"):
  with members:
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "compressive imaging via approximate message passing with image denoising"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "mismatched estimation in large linear systems"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "signal estimation with additive error metrics in compressed sensing"
    - "from denoising to compressed sensing"
    - "sparse estimation with the swept approximated message passing algorithm"

Connected through hyperedge HE-6 (induced by source paper "compression based compressed sensing"):
  with members:
    - "universal compressed sensing"
    - "from compression to compressed sensing"
    - "minimum complexity pursuit for universal compressed sensing"
    - "sharp recovery bounds for convex demixing with applications"
    - "sampling and recovery of pulse streams"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "stable restoration and separation of approximately sparse signals"
    - "optimal phase transitions in compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "universal compressed sensing"):
  with members:
    - "minimum complexity pursuit for universal compressed sensing"
    - "signal recovery on incoherent manifolds"
    - "stable image reconstruction using total variation minimization"
    - "minimum complexity pursuit stability analysis"
    - "sampling and recovery of pulse streams"
    - "optimal phase transitions in compressed sensing"
    - "minimum complexity pursuit"
    - "model based compressive sensing"

Connected through hyperedge HE-8 (induced by source paper "from compression to compressed sensing"):
  with members:
    - "minimum complexity pursuit for universal compressed sensing"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "lossy compression of discrete sources via the viterbi algorithm"
    - "minimum complexity pursuit stability analysis"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "stable restoration and separation of approximately sparse signals"
    - "optimal phase transitions 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)