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 "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.

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 "a fusion algorithm for solving bayesian decision problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a belief function based decision support system")
  Member "directed reduction algorithms and decomposable graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a tutorial on learning with bayesian networks")
  Member "d separation from theorems to algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "query the model precomputations for efficient inference with bayesian networks")
  Member "evidence absorption and propagation through evidence reversals":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "too fast causal inference under causal insufficiency")

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)