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 possibilistic model for qualitative sequential decision problems under uncertainty in partially observable environments"
  Abstract: In this article we propose a qualitative (ordinal) counterpart for the Partially Observable Markov Decision Processes model (POMDP) in which the uncertainty, as well as the preferences of the agent, are modeled by possibility distributions. This qualitative counterpart of the POMDP model relies on a possibilistic theory of decision under uncertainty, recently developed. One advantage of such a qualitative framework is its ability to escape from the classical obstacle of stochastic POMDPs, in which even with a finite state space, the obtained belief state space of the POMDP is infinite. Instead, in the possibilistic framework even if exponentially larger than the state space, the belief state space remains finite.

Connected through hyperedge HE-1 (induced by source paper "further improvements of determinization methods for fuzzy finite automata"):
  with members:
    - "supervisory control of fuzzy discrete event systems for simulation equivalence"
    - "brzozowski type determinization for fuzzy automata"
    - "determinization of fuzzy automata by means of the degrees of language inclusion"
    - "pomdps under probabilistic semantics"
    - "qualitative possibilistic mixed observable mdps"
    - "decidability of minimization of fuzzy automata"
    - "computation of the greatest simulations and bisimulations between fuzzy automata"
    - "bisimulations for fuzzy automata"

Connected through hyperedge HE-2 (induced by source paper "qualitative mdps and pomdps an order of magnitude approximation"):
  with members:
    - "an order of magnitude calculus"
    - "a qualitative linear utility theory for spohn s theory of epistemic beliefs"
    - "a general non probabilistic theory of inductive reasoning"
    - "from conditional oughts to qualitative decision theory"
    - "value function approximations for partially observable markov decision processes"
    - "conformant planning via symbolic model checking"

Connected through hyperedge HE-3 (induced by source paper "on the complexity of decision making in possibilistic decision trees"):
  with members:
    - "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"
    - "a comparison of axiomatic approaches to qualitative decision making using possibility theory"

Connected through hyperedge HE-4 (induced by source paper "qualitative decision making under possibilistic uncertainty toward more discriminating criteria"):
  with members:
    - "arguing for decisions a qualitative model of decision making"
    - "a comparison of axiomatic approaches to qualitative decision making using possibility theory"

Connected through hyperedge HE-5 (induced by source paper "an algebraic graphical model for decision with uncertainties feasibilities and utilities"):
  with members:
    - "qualitative models for decision under uncertainty without the commensurability assumption"
    - "an order of magnitude calculus"
    - "graphical models for preference and utility"
    - "plausibility measures a user s guide"
    - "a qualitative linear utility theory for spohn s theory of epistemic beliefs"
    - "context specific independence in bayesian networks"
    - "a general non probabilistic theory of inductive reasoning"
    - "general belief measures"

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)