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: "expectation propogation for approximate inference in dynamic bayesian networks"
  Abstract: We describe expectation propagation for approximate inference in dynamic Bayesian networks as a natural extension of Pearl s exact belief propagation.Expectation propagation IS a greedy algorithm, converges IN many practical cases, but NOT always.We derive a DOUBLE - loop algorithm, guaranteed TO converge TO a local minimum OF a Bethe free energy.Furthermore, we show that stable fixed points OF (damped) expectation propagation correspond TO local minima OF this free energy, but that the converse need NOT be the CASE .We illustrate the algorithms BY applying them TO switching linear dynamical systems AND discuss implications FOR approximate inference IN general Bayesian networks.

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"
    - "hybrid approximate message passing"
    - "state evolution for approximate message passing with non separable functions"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"

Connected through hyperedge HE-2 (induced by source paper "unifying message passing algorithms under the framework of constrained bethe free energy minimization"):
  with members:
    - "an iterative receiver for ofdm with sparsity based parametric channel estimation"
    - "hybrid vector perturbation precoding the blessing of approximate message passing"
    - "random access in c ran for user activity detection with limited capacity fronthaul"
    - "a bp mf ep based iterative receiver for joint phase noise estimation equalization and decoding"
    - "gaussian message passing iterative detection for mimo noma systems with massive access"
    - "message passing receiver for joint channel estimation and decoding in 3d massive mimo ofdm systems"
    - "optimality of large mimo detection via approximate message passing"
    - "s amp approximate message passing for general matrix ensembles"

Connected through hyperedge HE-3 (induced by source paper "a survey of stochastic simulation and optimization methods in signal processing"):
  with members:
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "finito a faster permutable incremental gradient method for big data problems"
    - "an empirical bayes approach to recovering linearly constrained non negative sparse signals"
    - "communication efficient distributed dual coordinate ascent"
    - "on the convergence of approximate message passing with arbitrary matrices"

Connected through hyperedge HE-4 (induced by source paper "distributed bayesian learning with stochastic natural gradient expectation propagation and the posterior server"):
  with members:
    - "stuck in a what adventures in weight space"
    - "overcoming catastrophic forgetting in neural networks"
    - "adam a method for stochastic optimization"
    - "neural variational inference and learning in belief networks"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"
    - "austerity in mcmc land cutting the metropolis hastings budget"

Connected through hyperedge HE-5 (induced by source paper "s amp approximate message passing for general matrix ensembles"):
  with members:
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "signal recovery using expectation consistent approximation for linear observations"
    - "applications of large random matrices in communications engineering"
    - "merging belief propagation and the mean field approximation a free energy approach"
    - "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"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "modeling transportation routines using hybrid dynamic mixed networks"):
  with members:
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "hybrid processing of beliefs and constraints"
    - "iterative join graph propagation"
    - "on finding minimal w cutset"
    - "approximate inference algorithms for hybrid bayesian networks with discrete constraints"
    - "a simple insight into iterative belief propagation s success"
    - "mixtures of deterministic probabilistic networks and their and or search space"

Connected through hyperedge HE-7 (induced by source paper "gibbs sampling in factorized continuous time markov processes"):
  with members:
    - "expectation propagation for approximate bayesian inference"
    - "continuous time bayesian networks"
    - "learning continuous time bayesian networks"
    - "continuous time markov networks"
    - "expectation propagation for continuous time bayesian networks"
    - "dimension reduction in singularly perturbed continuous time bayesian networks"
    - "reasoning at the right time granularity"

Connected through hyperedge HE-8 (induced by source paper "approximate inference algorithms for hybrid bayesian networks with discrete constraints"):
  with members:
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "iterative join graph propagation"
    - "on finding minimal w cutset"
    - "an empirical study of w cutset sampling for bayesian networks"
    - "a simple insight into iterative belief propagation s success"
    - "mixtures of deterministic probabilistic networks and their and or search space"

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.AI(Artificial Intelligence)