human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "compressive imaging via approximate message passing with image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "block coordinate regularization by denoising")
  Member "empirical bayes and full bayes for signal estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nonlinear function estimation with empirical bayes and approximate message passing")
  Member "compressed sensing via universal denoising and approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "understanding phase transitions via mutual information and mmse")
  Member "performance regions in compressed sensing from noisy measurements":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "performance limits with additive error metrics in noisy multimeasurement vector problems")
  Member "expectation maximization gaussian mixture approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "massive unsourced random access based on uncoupled compressive sensing another blessing of massive mimo")
  Member "minimum complexity pursuit for universal compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "solving inverse problems via auto encoders")
  Member "an mcmc approach to universal lossy compression of analog sources":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "statistical physics and information theory perspectives on linear inverse problems")
  Member "an mdl framework for sparse coding and dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning simple thresholded features with sparse support recovery")

Question: Please tell me which class the source paper 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)