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: "using potential influence diagrams for probabilistic inference and decision making"
  Abstract: The potential influence diagram is a generalization of the standard "conditional" influence diagram, a directed network representation for probabilistic inference and decision analysis [Ndilikilikesha, 1991]. It allows efficient inference calculations corresponding exactly to those on undirected graphs. In this paper, we explore the relationship between potential and conditional influence diagrams and provide insight into the properties of the potential influence diagram. In particular, we show how to convert a potential influence diagram into a conditional influence diagram, and how to view the potential influence diagram operations in terms of the conditional influence diagram.

Connected through hyperedge HE-1 (induced by source paper "myopic value of information in influence diagrams"):
  with members:
    - "value of evidence on influence diagrams"
    - "decision making using probabilistic inference methods"
    - "from influence diagrams to junction trees"
    - "a graph theoretic analysis of information value"

Connected through hyperedge HE-2 (induced by source paper "efficient value of information computation"):
  with members:
    - "decision making using probabilistic inference methods"
    - "from influence diagrams to junction trees"

Connected through hyperedge HE-3 (induced by source paper "from influence diagrams to junction trees"):
  with members:
    - "decision making using probabilistic inference methods"

Connected through hyperedge HE-4 (induced by source paper "hybrid influence diagrams using mixtures of truncated exponentials"):
  with members:
    - "solving influence diagrams using hugin shafer shenoy and lazy propagation"
    - "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"

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)