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: "a survey of stochastic simulation and optimization methods in signal processing"
  Abstract: Modern signal processing (SP) methods rely very heavily on probability and statistics to solve challenging SP problems. SP methods are now expected to deal with ever more complex models, requiring ever more sophisticated computational inference techniques. This has driven the development of statistical SP methods based on stochastic simulation and optimization. Stochastic simulation and optimization algorithms are computationally intensive tools for performing statistical inference in models that are analytically intractable and beyond the scope of deterministic inference methods. They have been recently successfully applied to many difficult problems involving complex statistical models and sophisticated (often Bayesian) statistical inference techniques. This survey paper offers an introduction to stochastic simulation and optimization methods in signal and image processing. The paper addresses a variety of high-dimensional Markov chain Monte Carlo (MCMC) methods as well as deterministic surrogate methods, such as variational Bayes, the Bethe approach, belief and expectation propagation and approximate message passing algorithms. It also discusses a range of optimization methods that have been adopted to solve stochastic problems, as well as stochastic methods for deterministic optimization. Subsequently, areas of overlap between simulation and optimization, in particular optimization-within-MCMC and MCMC-driven optimization are discussed.

Connected through hyperedge HE-1 (induced by source paper "inference with deep generative priors in high dimensions"):
  with members:
    - "bilinear recovery using adaptive vector amp"
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "asymptotics of map inference in deep networks"
    - "entropy and mutual information in models of deep neural networks"
    - "inverting deep generative models one layer at a time"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "finite sample analysis of approximate message passing algorithms"
    - "sunlayer stable denoising with generative networks"

Connected through hyperedge HE-2 (induced by source paper "understanding phase transitions via mutual information and mmse"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "multi layer generalized linear estimation"
    - "additivity of information in multilayer networks via additive gaussian noise transforms"
    - "generalized expectation consistent signal recovery for nonlinear measurements"
    - "dynamical functional theory for compressed sensing"
    - "phase transitions optimal errors and optimality of message passing in generalized linear models"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "expectation consistent approximate inference generalizations and convergence"

Connected through hyperedge HE-3 (induced by source paper "infinite mixture of inverted dirichlet distributions"):
  with members:
    - "decorrelation of neutral vector variables theory and applications"
    - "the role of data analysis in the development of intelligent energy networks"

Connected through hyperedge HE-4 (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"
    - "learning and free energies for vector approximate message passing"
    - "parametric bilinear generalized approximate message passing"

Connected through hyperedge HE-5 (induced by source paper "expectation consistent approximate inference generalizations and convergence"):
  with members:
    - "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"
    - "s amp approximate message passing for general matrix ensembles"

Connected through hyperedge HE-6 (induced by source paper "vector approximate message passing for the generalized linear model"):
  with members:
    - "hybrid approximate message passing"
    - "expectation consistent approximate inference generalizations and convergence"
    - "learning and free energies for vector approximate message passing"
    - "denoising based vector approximate message passing"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "compressive phase retrieval via generalized approximate message passing"
    - "s amp approximate message passing for general matrix ensembles"
    - "on the convergence of approximate message passing with arbitrary matrices"

Connected through hyperedge HE-7 (induced by source paper "hyperspectral unmixing via turbo bilinear approximate message passing"):
  with members:
    - "hierarchical clustering of hyperspectral images using rank two nonnegative matrix factorization"
    - "an empirical bayes approach to recovering linearly constrained non negative sparse signals"
    - "bilinear generalized approximate message passing part i derivation"
    - "variational free energies for compressed sensing"
    - "a factor graph approach to joint ofdm channel estimation and decoding in impulsive noise environments"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "loopy belief propagation for approximate inference an empirical study"
    - "expectation maximization gaussian mixture approximate message passing"

Connected through hyperedge HE-8 (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"
    - "learning and free energies for vector approximate message passing"
    - "parametric bilinear generalized approximate message passing"

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)