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 "on the complexity of decision making in possibilistic decision trees"):
  Abstract: When the information about uncertainty cannot be quantified in a simple, probabilistic way, the topic of possibilistic decision theory is often a natural one to consider. The development of possibilistic decision theory has lead to a series of possibilistic criteria, e.g pessimistic possibilistic qualitative utility, possibilistic likely dominance, binary possibilistic utility and possibilistic Choquet integrals. This paper focuses on sequential decision making in possibilistic decision trees. It proposes a complexity study of the problem of finding an optimal strategy depending on the monotonicity property of the optimization criteria which allows the application of dynamic programming that offers a polytime reduction of the decision problem. It also shows that possibilistic Choquet integrals do not satisfy this property, and that in this case the optimization problem is NP - hard.

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 "an algebraic graphical model for decision with uncertainties feasibilities and utilities")
  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 "a general non probabilistic theory of inductive reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "factored probabilistic belief tracking")
  Member "from conditional oughts to qualitative decision theory":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a qualitative linear utility theory for spohn s theory of epistemic beliefs")
  Member "a comparison of axiomatic approaches to qualitative decision making using possibility theory":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "axiomatic foundations for a class of generalized expected utility algebraic expected utility")

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)