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 "an algebraic graphical model for decision with uncertainties feasibilities and utilities"):
  Abstract: Numerous formalisms and dedicated algorithms have been designed in the last decades to model and solve decision making problems. Some formalisms, such as constraint networks, can express "simple" decision problems, while others are designed to take into account uncertainties, unfeasible decisions, and utilities. Even in a single formalism, several variants are often proposed to model different types of uncertainty (probability, possibility...) or utility (additive or not). In this article, we introduce an algebraic graphical model that encompasses a large number of such formalisms: (1) we first adapt previous structures from Friedman, Chu and Halpern for representing uncertainty, utility, and expected utility in order to deal with generic forms of sequential decision making; (2) on these structures, we then introduce composite graphical models that express information via variables linked by "local" functions, thanks to conditional independence; (3) on these graphical models, we finally define a simple class of queries which can represent various scenarios in terms of observabilities and controllabilities. A natural decision-tree semantics for such queries is completed by an equivalent operational semantics, which induces generic algorithms. The proposed framework, called the Plausibility-Feasibility-Utility (PFU) framework, not only provides a better understanding of the links between existing formalisms, but it also covers yet unpublished frameworks (such as possibilistic influence diagrams) and unifies formalisms such as quantified boolean formulas and influence diagrams. Our backtrack and variable elimination generic algorithms are a first step towards unified algorithms.

Includes members:
  Member "a possibilistic model for qualitative sequential decision problems under uncertainty in partially observable environments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "further improvements of determinization methods for fuzzy finite automata")
  Member "qualitative models for decision under uncertainty without the commensurability assumption":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the complexity of decision making in possibilistic decision trees")
  Member "an order of magnitude calculus":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a qualitative linear utility theory for spohn s theory of epistemic beliefs")
  Member "graphical models for preference and utility":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "axiomatization of an importance index for k ary games")
  Member "plausibility measures a user s guide":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compact representations of extended causal models")
  Member "a qualitative linear utility theory for spohn s theory of epistemic beliefs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a comparison of axiomatic approaches to qualitative decision making using possibility theory")
  Member "context specific independence in bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "abstracting probabilistic models a logical perspective")
  Member "a general non probabilistic theory of inductive reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "factored probabilistic belief tracking")

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)