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: "nested junction trees"
  Abstract: The efficiency of inference in both the Hugin and, most notably, the Shafer-Shenoy architectures can be improved by exploiting the independence relations induced by the incoming messages of a clique. That is, the message to be sent from a clique can be computed via a factorization of the clique potential in the form of a junction tree. In this paper we show that by exploiting such nested junction trees in the computation of messages both space and time costs of the conventional propagation methods may be reduced. The paper presents a structured way of exploiting the nested junction trees technique to achieve such reductions. The usefulness of the method is emphasized through a thorough empirical evaluation involving ten large real-world Bayesian networks and the Hugin inference algorithm.

Connected through hyperedge HE-1 (induced by source paper "topological parameters for time space tradeoff"):
  with members:
    - "graphical models for preference and utility"
    - "a probabilistic approach to hierarchical model based diagnosis"
    - "an evaluation of structural parameters for probabilistic reasoning results on benchmark circuits"
    - "optimal junction trees"
    - "global conditioning for probabilistic inference in belief networks"
    - "a sufficiently fast algorithm for finding close to optimal junction trees"

Connected through hyperedge HE-2 (induced by source paper "solving influence diagrams using hugin shafer shenoy and lazy propagation"):
  with members:
    - "from influence diagrams to junction trees"
    - "a comparison of lauritzen spiegelhalter hugin and shenoy shafer architectures for computing marginals of probability distributions"
    - "evaluating influence diagrams using limids"

Connected through hyperedge HE-3 (induced by source paper "reasoning at the right time granularity"):
  with members:
    - "expectation propagation for approximate bayesian inference"
    - "continuous time bayesian networks"
    - "expectation propagation for continuous time 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)