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: "hybrid approximate message passing"
  Abstract: Gaussian and quadratic approximations of message passing algorithms on graphs have attracted considerable recent attention due to their computational simplicity, analytic tractability, and wide applicability in optimization and statistical inference problems. This paper presents a systematic framework for incorporating such approximate message passing (AMP) methods in general graphical models. The key concept is a partition of dependencies of a general graphical model into strong and weak edges, with the weak edges representing small, linearizable couplings of variables. AMP approximations based on the central limit theorem can be readily applied to aggregates of many weak edges and integrated with standard message passing updates on the strong edges. The resulting algorithm, which we call hybrid generalized approximate message passing (HyGAMP), can yield significantly simpler implementations of sum-product and max-sum loopy belief propagation. By varying the partition of strong and weak edges, a performance-complexity tradeoff can be achieved. Group sparsity and multinomial logistic regression problems are studied as examples of the proposed methodology.

Connected through hyperedge HE-1 (induced by source paper "bilinear recovery using adaptive vector amp"):
  with members:
    - "regularization by denoising clarifications and new interpretations"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "near optimal matrix recovery from random linear measurements"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "additivity of information in multilayer networks via additive gaussian noise transforms"
    - "state evolution for approximate message passing with non separable functions"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "expectation consistent approximate inference generalizations and convergence"

Connected through hyperedge HE-2 (induced by source paper "plug and play methods for magnetic resonance imaging"):
  with members:
    - "bilinear recovery using adaptive vector amp"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "an online plug and play algorithm for regularized image reconstruction"
    - "image reconstruction by domain transform manifold learning"
    - "high quality automatic foreground extraction using consensus equilibrium"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-3 (induced by source paper "a new insight into gamp and amp"):
  with members:
    - "capacity achieving mimo noma iterative lmmse detection"
    - "capacity optimality of amp in coded systems"
    - "a unified bayesian inference framework for generalized linear models"
    - "bilinear adaptive generalized vector approximate message passing"
    - "gaussian message passing for overloaded massive mimo noma"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "beam on graph simultaneous channel estimation for mmwave mimo systems with multiple users"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"

Connected through hyperedge HE-4 (induced by source paper "capacity achieving mimo noma iterative lmmse detection"):
  with members:
    - "near gaussian multiple access channel capacity detection and decoding"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "gaussian message passing iterative detection for mimo noma systems with massive access"
    - "a general mimo framework for noma downlink and uplink transmission based on signal alignment"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "achievable rates of mimo systems with linear precoding and iterative lmmse detection"
    - "hybrid approximate message passing"
    - "estimation in gaussian noise properties of the minimum mean square error"

Connected through hyperedge HE-5 (induced by source paper "regularization by denoising clarifications and new interpretations"):
  with members:
    - "local kernels that approximate bayesian regularization and proximal operators"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "scene adapted plug and play algorithm with guaranteed convergence applications to data fusion in imaging"
    - "an online plug and play algorithm for regularized image reconstruction"
    - "acceleration of red via vector extrapolation"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "plug and play unplugged optimization free reconstruction using consensus equilibrium"

Connected through hyperedge HE-6 (induced by source paper "bilinear adaptive generalized vector approximate message passing"):
  with members:
    - "bilinear recovery using adaptive vector amp"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "parametric bilinear generalized approximate message passing"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "vector approximate message passing for the generalized linear model"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "self calibration and biconvex compressive sensing"

Connected through hyperedge HE-7 (induced by source paper "the mutual information in random linear estimation beyond i i d matrices"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "the stochastic interpolation method a simple scheme to prove replica formulas in bayesian inference"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "the layered structure of tensor estimation and its mutual information"
    - "multi layer generalized linear estimation"
    - "additivity of information in multilayer networks via additive gaussian noise transforms"
    - "phase transitions optimal errors and optimality of message passing in generalized linear models"
    - "mutual information and optimality of approximate message passing in random linear estimation"

Connected through hyperedge HE-8 (induced by source paper "grid less variational bayesian inference of line spectral from quantized samples"):
  with members:
    - "a unified bayesian inference framework for generalized linear models"
    - "variational bayesian inference of line spectral estimation with multiple measurement vectors"
    - "bilinear adaptive generalized vector approximate message passing"
    - "sparse methods for direction of arrival estimation"
    - "newtonized orthogonal matching pursuit for line spectrum estimation with multiple measurement vectors"
    - "variational bayesian inference of line spectra"
    - "newtonized orthogonal matching pursuit frequency estimation over the continuum"
    - "vector approximate message passing for the generalized linear model"

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)