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 scheme for approximating probabilistic inference"
  Abstract: This paper describes a class of probabilistic approximation algorithms based on bucket elimination which offer adjustable levels of accuracy and efficiency. We analyze the approximation for several tasks: finding the most probable explanation, belief updating and finding the maximum a posteriori hypothesis. We identify regions of completeness and provide preliminary empirical evaluation on randomly generated networks.

Connected through hyperedge HE-1 (induced by source paper "tractable inference for complex stochastic processes"):
  with members:
    - "stochastic simulation algorithms for dynamic probabilistic networks"
    - "a computational scheme for reasoning in dynamic probabilistic networks"
    - "dynamic network models for forecasting"
    - "tradeoffs in constructing and evaluating temporal influence diagrams"

Connected through hyperedge HE-2 (induced by source paper "mini bucket heuristics for improved search"):
  with members:
    - "a new algorithm for finding map assignments to belief networks"
    - "empirical evaluation of approximation algorithms for probabilistic decoding"

Connected through hyperedge HE-3 (induced by source paper "systematic vs non systematic algorithms for solving the mpe task"):
  with members:
    - "iterative join graph propagation"

Connected through hyperedge HE-4 (induced by source paper "iterative join graph propagation"):
  with members:
    - "belief optimization for binary networks a stable alternative to loopy belief propagation"

Connected through hyperedge HE-5 (induced by source paper "join graph propagation algorithms"):
  with members:
    - "expectation propagation for approximate bayesian inference"
    - "value elimination bayesian inference via backtracking search"
    - "iterative join graph propagation"
    - "an importance sampling algorithm based on evidence pre propagation"
    - "a variational approach for approximating bayesian networks by edge deletion"
    - "structured region graphs morphing ep into gbp"
    - "a simple insight into iterative belief propagation s success"
    - "node splitting a scheme for generating upper bounds in 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)