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: "iterative join graph propagation"
  Abstract: The paper presents an iterative version of join-tree clustering that applies the message passing of join-tree clustering algorithm to join-graphs rather than to join-trees, iteratively. It is inspired by the success of Pearl's belief propagation algorithm as an iterative approximation scheme on one hand, and by a recently introduced mini-clustering i. success as an anytime approximation method, on the other. The proposed Iterative Join-graph Propagation IJGP belongs to the class of generalized belief propagation methods, recently proposed using analogy with algorithms in statistical physics. Empirical evaluation of this approach on a number of problem classes demonstrates that even the most time-efficient variant is almost always superior to IBP and MC i, and is sometimes more accurate by as much as several orders of magnitude.

Connected through hyperedge HE-1 (induced by source paper "approximate knowledge compilation by online collapsed importance sampling"):
  with members:
    - "on relaxing determinism in arithmetic circuits"
    - "loopy belief propagation for approximate inference an empirical study"
    - "a differential approach to inference in bayesian networks"
    - "on bayesian network approximation by edge deletion"
    - "on the robustness of most probable explanations"
    - "cutset sampling for bayesian networks"

Connected through hyperedge HE-2 (induced by source paper "representing meaning with a combination of logical and distributional models"):
  with members:
    - "skip thought vectors"
    - "compositional semantic parsing on semi structured tables"
    - "teaching machines to read and comprehend"
    - "the frobenius anatomy of word meanings i subject and object relative pronouns"
    - "efficient estimation of word representations in vector space"
    - "hinge loss markov random fields convex inference for structured prediction"
    - "probabilistic similarity logic"
    - "probabilistic theorem proving"

Connected through hyperedge HE-3 (induced by source paper "representing meaning with a combination of logical form and vectors"):
  with members:
    - "compositional semantic parsing on semi structured tables"
    - "teaching machines to read and comprehend"
    - "the frobenius anatomy of word meanings i subject and object relative pronouns"
    - "efficient estimation of word representations in vector space"
    - "probabilistic similarity logic"
    - "probabilistic theorem proving"
    - "experimental support for a categorical compositional distributional model of meaning"
    - "learning dependency based compositional semantics"

Connected through hyperedge HE-4 (induced by source paper "dual decomposition from the perspective of relax compensate and then recover"):
  with members:
    - "a differential approach to inference in bayesian networks"
    - "approximating the partition function by deleting and then correcting for model edges"
    - "tightening lp relaxations for map using message passing"
    - "lagrangian relaxation for map estimation in graphical models"

Connected through hyperedge HE-5 (induced by source paper "generalized belief propagation on tree robust structured region graphs"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "belief optimization for binary networks a stable alternative to loopy belief propagation"
    - "sufficient conditions for convergence of loopy belief propagation"
    - "on the choice of regions for generalized belief propagation"
    - "convergent message passing algorithms for inference over general graphs with convex free energies"
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "structured region graphs morphing ep into gbp"
    - "convergent message passing algorithms a unifying view"

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"
    - "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"
    - "expectation propogation for approximate inference in dynamic bayesian networks"

Connected through hyperedge HE-7 (induced by source paper "a variational approach for approximating bayesian networks by edge deletion"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "variational approximations between mean field theory and the junction tree algorithm"
    - "on bayesian network approximation by edge deletion"
    - "a generalized mean field algorithm for variational inference in exponential families"
    - "complexity results and approximation strategies for map explanations"
    - "exploiting causal independence in bayesian network inference"

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"
    - "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"
    - "expectation propogation for approximate inference in dynamic bayesian networks"

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)