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: "solving influence diagrams using hugin shafer shenoy and lazy propagation"
  Abstract: In this paper we compare three different architectures for the evaluation of influence diagrams: HUGIN, Shafer-Shenoy, and Lazy Evaluation architecture. The computational complexity of the architectures are compared on the LImited Memory Influence Diagram (LIMID): a diagram where only the requiste information for the computation of the optimal policies are depicted. Because the requsite information is explicitly represented in the LIMID the evaluation can take advantage of it, and significant savings in computational can be obtained. In this paper we show how the obtained savings is considerably increased when the computations performed on the LIMID is according to the Lazy Evaluation scheme.

Connected through hyperedge HE-1 (induced by source paper "solving limited memory influence diagrams using branch and bound search"):
  with members:
    - "decision making using probabilistic inference methods"
    - "from influence diagrams to junction trees"
    - "an anytime algorithm for decision making under uncertainty"
    - "efficient value of information computation"
    - "solving multistage influence diagrams using branch and bound search"
    - "strategy selection in influence diagrams using imprecise probabilities"
    - "solving limited memory influence diagrams"

Connected through hyperedge HE-2 (induced by source paper "belief propagation for structured decision making"):
  with members:
    - "decision making using probabilistic inference methods"
    - "probabilistic inference in influence diagrams"
    - "from influence diagrams to junction trees"
    - "map estimation linear programming and belief propagation with convex free energies"
    - "a new class of upper bounds on the log partition function"
    - "join graph propagation algorithms"

Connected through hyperedge HE-3 (induced by source paper "hybrid influence diagrams using mixtures of truncated exponentials"):
  with members:
    - "from influence diagrams to junction trees"
    - "mixtures of gaussians and minimum relative entropy techniques for modeling continuous uncertainties"
    - "lazy evaluation of symmetric bayesian decision problems"
    - "using potential influence diagrams for probabilistic inference and decision making"

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)