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: "probabilistic inference in influence diagrams"
  Abstract: This paper is about reducing influence diagram (ID) evaluation into Bayesian network (BN) inference problems. Such reduction is interesting because it enables one to readily use one's favorite BN inference algorithm to efficiently evaluate IDs. Two such reduction methods have been proposed previously (Cooper 1988, Shachter and Peot 1992). This paper proposes a new method. The BN inference problems induced by the mew method are much easier to solve than those induced by the two previous methods.

Connected through hyperedge HE-1 (induced by source paper "belief propagation for structured decision making"):
  with members:
    - "decision making using probabilistic inference methods"
    - "solving influence diagrams using hugin shafer shenoy and lazy propagation"
    - "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-2 (induced by source paper "solving multistage influence diagrams using branch and bound search"):
  with members:
    - "from influence diagrams to junction trees"
    - "an algorithm for finding minimum d separating sets in belief networks"
    - "an anytime algorithm for decision making under uncertainty"
    - "evaluating influence diagrams with decision circuits"
    - "unconstrained influence diagrams"

Connected through hyperedge HE-3 (induced by source paper "strategy selection in influence diagrams using imprecise probabilities"):
  with members:
    - "from influence diagrams to junction trees"
    - "independence concepts for convex sets of probabilities"
    - "complexity results and approximation strategies for map explanations"

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)