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: "accuracy bounds for belief propagation"
  Abstract: The belief propagation (BP) algorithm is widely applied to perform approximate inference on arbitrary graphical models, in part due to its excellent empirical properties and performance. However, little is known theoretically about when this algorithm will perform well. Using recent analysis of convergence and stability properties in BP and new results on approximations in binary systems, we derive a bound on the error in BP's estimates for pairwise Markov random fields over discrete valued random variables. Our bound is relatively simple to compute, and compares favorably with a previous method of bounding the accuracy of BP.

Connected through hyperedge HE-1 (induced by source paper "anytime exact belief propagation"):
  with members:
    - "probabilistic inference modulo theories"
    - "bayesian logic programs"

Connected through hyperedge HE-2 (induced by source paper "bethe bounds and approximating the global optimum"):
  with members:
    - "belief optimization for binary networks a stable alternative to loopy belief propagation"
    - "constrained approximate maximum entropy learning of markov random fields"
    - "what cannot be learned with bethe approximations"
    - "a new class of upper bounds on the log partition function"
    - "the bethe partition function of log supermodular graphical models"
    - "sufficient conditions for convergence of the sum product algorithm"
    - "variational probabilistic inference and the qmr dt network"

Connected through hyperedge HE-3 (induced by source paper "active tuples based scheme for bounding posterior beliefs"):
  with members:
    - "context specific approximation in probabilistic inference"
    - "large deviation methods for approximate probabilistic inference"
    - "bounded conditioning flexible inference for decisions under scarce resources"
    - "knowledge engineering for large belief networks"
    - "a sufficiently fast algorithm for finding close to optimal junction trees"
    - "approximate decomposition a method for bounding and estimating probabilistic and deterministic queries"
    - "an empirical study of w cutset sampling for bayesian networks"
    - "a new class of upper bounds on the log partition function"

Connected through hyperedge HE-4 (induced by source paper "message error analysis of loopy belief propagation for the sum product algorithm"):
  with members:
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "loopy belief propogation and gibbs measures"
    - "improved dynamic schedules for belief propagation"
    - "graph zeta function in the bethe free energy and loopy belief propagation"
    - "sufficient conditions for convergence of the sum product algorithm"

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)