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: "a comparison of axiomatic approaches to qualitative decision making using possibility theory"
  Abstract: In this paper we analyze two recent axiomatic approaches proposed by Dubois et al and by Giang and Shenoy to qualitative decision making where uncertainty is described by possibility theory. Both axiomtizations are inspired by von Neumann and Morgenstern's system of axioms for the case of probability theory. We show that our approach naturally unifies two axiomatic systems that correspond respectively to pessimistic and optimistic decision criteria proposed by Dubois et al. The simplifying unification is achieved by (i) replacing axioms that are supposed to reflect two informational attitudes (uncertainty aversion and uncertainty attraction) by an axiom that imposes order on set of standard lotteries and (ii) using a binary utility scale in which each utility level is represented by a pair of numbers.

Connected through hyperedge HE-1 (induced by source paper "axiomatic foundations for a class of generalized expected utility algebraic expected utility"):
  with members:
    - "an order of magnitude calculus"
    - "plausibility measures a user s guide"
    - "a qualitative linear utility theory for spohn s theory of epistemic beliefs"
    - "a general non probabilistic theory of inductive reasoning"
    - "general belief measures"
    - "decision making for symbolic probability"
    - "qualitative decision making under possibilistic uncertainty toward more discriminating criteria"
    - "decision making with partially consonant belief functions"

Connected through hyperedge HE-2 (induced by source paper "on the complexity of decision making in possibilistic decision trees"):
  with members:
    - "a possibilistic model for qualitative sequential decision problems under uncertainty in partially observable environments"
    - "qualitative models for decision under uncertainty without the commensurability assumption"
    - "an order of magnitude calculus"
    - "a general non probabilistic theory of inductive reasoning"
    - "from conditional oughts to qualitative decision theory"

Connected through hyperedge HE-3 (induced by source paper "qualitative decision making under possibilistic uncertainty toward more discriminating criteria"):
  with members:
    - "a possibilistic model for qualitative sequential decision problems under uncertainty in partially observable environments"
    - "arguing for decisions a qualitative model of decision making"

Connected through hyperedge HE-4 (induced by source paper "decision making for symbolic probability"):
  with members:
    - "decision making with partially consonant belief functions"

Connected through hyperedge HE-5 (induced by source paper "statistical decisions using likelihood information without prior probabilities"):
  with members:
    - "a qualitative linear utility theory for spohn s theory of epistemic beliefs"

Connected through hyperedge HE-6 (induced by source paper "great expectations part ii generalized expected utility as a universal decision rule"):
  with members:
    - "plausibility measures a user s guide"
    - "great expectations part i on the customizability of generalized expected utility"
    - "expected qualitative utility maximization"
    - "conditional plausibility measures and bayesian networks"

Connected through hyperedge HE-7 (induced by source paper "great expectations part i on the customizability of generalized expected utility"):
  with members:
    - "generalized qualitative probability savage revisited"
    - "plausibility measures a user s guide"
    - "great expectations part ii generalized expected utility as a universal decision rule"
    - "conditional plausibility measures and bayesian networks"

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)