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: "refinement and coarsening of bayesian networks"
  Abstract: In almost all situation assessment problems, it is useful to dynamically contract and expand the states under consideration as assessment proceeds. Contraction is most often used to combine similar events or low probability events together in order to reduce computation. Expansion is most often used to make distinctions of interest which have significant probability in order to improve the quality of the assessment. Although other uncertainty calculi, notably Dempster-Shafer [Shafer, 1976], have addressed these operations, there has not yet been any approach of refining and coarsening state spaces for the Bayesian Network technology. This paper presents two operations for refining and coarsening the state space in Bayesian Networks. We also discuss their practical implications for knowledge acquisition.

Connected through hyperedge HE-1 (induced by source paper "state space abstraction for anytime evaluation of probabilistic networks"):
  with members:
    - "utility based abstraction and categorization"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "using tree decomposable structures to approximate belief networks"
    - "bounded conditioning flexible inference for decisions under scarce resources"
    - "ideal reformulation of belief networks"
    - "ideal a software package for analysis of influence diagrams"
    - "incremental probabilistic inference"

Connected through hyperedge HE-2 (induced by source paper "dynamic network updating techniques for diagnostic reasoning"):
  with members:
    - "a causal bayesian model for the diagnosis of appendicitis"
    - "model based influence diagrams for machine vision"
    - "ideal reformulation of belief networks"
    - "problem formulation as the reduction of a decision model"
    - "ideal a software package for analysis of influence diagrams"
    - "dynamic construction of belief networks"

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

Connected through hyperedge HE-4 (induced by source paper "reasoning about the value of decision model refinement methods and application"):
  with members:
    - "ideal a software package for analysis of influence diagrams"
    - "integrating model construction and evaluation"
    - "reasoning about beliefs and actions under computational resource constraints"

Connected through hyperedge HE-5 (induced by source paper "exploiting model equivalences for solving interactive dynamic influence diagrams"):
  with members:
    - "graphical models for game theory"
    - "evaluating influence diagrams using limids"
    - "solving multistage influence diagrams using branch and bound search"
    - "knowledge combination in graphical multiagent model"
    - "networks of influence diagrams a formalism for representing agents beliefs and decision making processes"
    - "a framework for sequential planning in multi agent settings"

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)