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: "reasoning about the value of decision model refinement methods and application"
  Abstract: We investigate the value of extending the completeness of a decision model along different dimensions of refinement. Specifically, we analyze the expected value of quantitative, conceptual, and structural refinement of decision models. We illustrate the key dimensions of refinement with examples. The analyses of value of model refinement can be used to focus the attention of an analyst or an automated reasoning system on extensions of a decision model associated with the greatest expected value.

Connected through hyperedge HE-1 (induced by source paper "a graph theoretic analysis of information value"):
  with members:
    - "reformulating inference problems through selective conditioning"
    - "efficient decision theoretic planning techniques and empirical analysis"
    - "ideal reformulation of belief networks"
    - "dynamic construction of belief networks"
    - "chain graphs for learning"
    - "state space abstraction for anytime evaluation of probabilistic networks"
    - "representing context sensitive knowledge in a network formalism a preliminary report"
    - "localized partial evaluation of belief networks"

Connected through hyperedge HE-2 (induced by source paper "an anytime algorithm for decision making under uncertainty"):
  with members:
    - "decision making using probabilistic inference methods"
    - "utility based abstraction and categorization"
    - "some properties of joint probability distributions"
    - "the compilation of decision models"
    - "flexible policy construction by information refinement"
    - "two procedures for compiling influence diagrams"

Connected through hyperedge HE-3 (induced by source paper "learning the dimensionality of hidden variables"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "refinement and coarsening of bayesian networks"
    - "the bayesian structural em algorithm"
    - "state space abstraction for anytime evaluation of probabilistic networks"

Connected through hyperedge HE-4 (induced by source paper "probabilistic conceptual network a belief representation scheme for utility based categorization"):
  with members:
    - "problem formulation as the reduction of a decision model"
    - "dynamic construction of belief networks"
    - "representing context sensitive knowledge in a network formalism a preliminary report"

Connected through hyperedge HE-5 (induced by source paper "time critical dynamic decision making"):
  with members:
    - "tradeoffs in constructing and evaluating temporal influence diagrams"

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)