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: "estimation with random linear mixing belief propagation and compressed sensing"
  Abstract: We apply Guo and Wang's relaxed belief propagation (BP) method to the estimation of a random vector from linear measurements followed by a componentwise probabilistic measurement channel. Relaxed BP uses a Gaussian approximation in standard BP to obtain significant computational savings for dense measurement matrices. The main contribution of this paper is to extend the relaxed BP method and analysis to general (non-AWGN) output channels. Specifically, we present detailed equations for implementing relaxed BP for general channels and show that relaxed BP has an identical asymptotic large sparse limit behavior as standard BP, as predicted by the Guo and Wang's state evolution (SE) equations. Applications are presented to compressed sensing and estimation with bounded noise.

Connected through hyperedge HE-1 (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-2 (induced by source paper "hybrid approximate message passing"):
  with members:
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "decoding from pooled data phase transitions of message passing"
    - "random access in c ran for user activity detection with limited capacity fronthaul"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "expectation consistent approximate inference generalizations and convergence"
    - "sparse multinomial logistic regression via approximate message passing"
    - "a survey of stochastic simulation and optimization methods in signal processing"
    - "learning and free energies for vector approximate message passing"

Connected through hyperedge HE-3 (induced by source paper "phase transitions and sample complexity in bayes optimal matrix factorization"):
  with members:
    - "an overview of low rank matrix recovery from incomplete observations"
    - "mmse of probabilistic low rank matrix estimation universality with respect to the output channel"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "information theoretically optimal sparse pca"
    - "bilinear generalized approximate message passing part i derivation"
    - "variational free energies for compressed sensing"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "phase diagram and approximate message passing for blind calibration and dictionary learning"

Connected through hyperedge HE-4 (induced by source paper "expectation consistent approximate inference generalizations and convergence"):
  with members:
    - "a survey of stochastic simulation and optimization methods in signal processing"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "compressive phase retrieval via generalized approximate message passing"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "s amp for non linear observation models"
    - "binary linear classification and feature selection via generalized approximate message passing"
    - "scalable inference for neuronal connectivity from calcium imaging"

Connected through hyperedge HE-5 (induced by source paper "inferring sparsity compressed sensing using generalized restricted boltzmann machines"):
  with members:
    - "hybrid approximate message passing"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "approximate message passing with restricted boltzmann machine priors"
    - "learning optimal nonlinearities for iterative thresholding algorithms"
    - "a survey of sparse representation algorithms and applications"
    - "a deep learning approach to structured signal recovery"
    - "hybrid approximate message passing"
    - "hybrid approximate message passing with applications to structured sparsity"

Connected through hyperedge HE-6 (induced by source paper "exit chart analysis of turbo compressed sensing using message passing dequantization"):
  with members:
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"
    - "iterative detection for compressive sensing turbo cs"
    - "joint source channel vector quantization for compressed sensing"
    - "message passing de quantization with applications to compressed sensing"
    - "regime change bit depth versus measurement rate in compressive sensing"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "inference for generalized linear models via alternating directions and bethe free energy minimization"):
  with members:
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "an empirical bayes approach to recovering linearly constrained non negative sparse signals"
    - "bilinear generalized approximate message passing part i derivation"
    - "on the convergence of approximate message passing with arbitrary matrices"
    - "variational free energies for compressed sensing"
    - "sparse estimation with the swept approximated message passing algorithm"
    - "on convergence of approximate message passing"
    - "fixed points of generalized approximate message passing with arbitrary matrices"

Connected through hyperedge HE-8 (induced by source paper "approximate message passing with unitary transformation"):
  with members:
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "on the convergence of approximate message passing with arbitrary matrices"
    - "sparse estimation with the swept approximated message passing algorithm"
    - "on convergence of approximate message passing"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing i motivation and construction"

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)