human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "lazy evaluation of symmetric bayesian decision problems"):
  Abstract: Solving symmetric Bayesian decision problems is a computationally intensive task to perform regardless of the algorithm used. In this paper we propose a method for improving the efficiency of algorithms for solving Bayesian decision problems. The method is based on the principle of lazy evaluation - a principle recently shown to improve the efficiency of inference in Bayesian networks. The basic idea is to maintain decompositions of potentials and to postpone computations for as long as possible. The efficiency improvements obtained with the lazy evaluation based method is emphasized through examples. Finally, the lazy evaluation based method is compared with the hugin and valuation-based systems architectures for solving symmetric Bayesian decision problems.

Includes members:
  Member "decision making using probabilistic inference methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "influence diagrams for the optimization of a vehicle speed profile")
  Member "from influence diagrams to junction trees":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a symbolic algebra for the computation of expected utilities in multiplicative influence diagrams")
  Member "d separation from theorems to algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "query the model precomputations for efficient inference with bayesian networks")
  Member "lazy propagation in junction trees":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inference in multiply sectioned bayesian networks with extended shafer shenoy and lazy propagation")
  Member "a method for using belief networks as influence diagrams":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "influence diagrams for the optimization of a vehicle speed profile")

Question: Please tell me which class the source paper 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)