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 "propagation of 2 monotone lower probabilities on an undirected graph"):
  Abstract: Lower and upper probabilities, also known as Choquet capacities, are widely used as a convenient representation for sets of probability distributions. This paper presents a graphical decomposition and exact propagation algorithm for computing marginal posteriors of 2-monotone lower probabilities (equivalently, 2-alternating upper probabilities).

Includes members:
  Member "independence with lower and upper probabilities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "separation properties of sets of probability measures")
  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 "constraint propagation with imprecise conditional probabilities":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "magic inference rules for probabilistic deduction under taxonomic knowledge")
  Member "minimal assumption distribution propagation in belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bayesian error bars for belief net inference")
  Member "a new approach to updating beliefs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an interval valued utility theory for decision making with dempster shafer belief functions")
  Member "optimal junction trees":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning the relation between code features and code transforms with structured prediction")
  Member "quasi bayesian strategies for efficient plan generation application to the planning to observe problem":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "independence with lower and upper probabilities")
  Member "an efficient implementation of belief function propagation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a belief function based decision support system")

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)