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: "ideal a software package for analysis of influence diagrams"
  Abstract: IDEAL (Influence Diagram Evaluation and Analysis in Lisp) is a software environment for creation and evaluation of belief networks and influence diagrams. IDEAL is primarily a research tool and provides an implementation of many of the latest developments in belief network and influence diagram evaluation in a unified framework. This paper describes IDEAL and some lessons learned during its development.

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"
    - "refinement and coarsening of bayesian networks"
    - "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"
    - "refinement and coarsening of bayesian networks"
    - "dynamic construction of belief networks"

Connected through hyperedge HE-3 (induced by source paper "reasoning about the value of decision model refinement methods and application"):
  with members:
    - "refinement and coarsening of bayesian networks"
    - "integrating model construction and evaluation"
    - "reasoning about beliefs and actions under computational resource constraints"

Connected through hyperedge HE-4 (induced by source paper "generating bayesian networks from probability logic knowledge bases"):
  with members:
    - "using first order probability logic for the construction of bayesian networks"
    - "dynamic construction of belief networks"
    - "representing bayesian networks within probabilistic horn abduction"

Connected through hyperedge HE-5 (induced by source paper "abstraction in belief networks the role of intermediate states in diagnostic reasoning"):
  with members:
    - "accounting for context in plan recognition with application to traffic monitoring"
    - "state space abstraction for anytime evaluation of probabilistic networks"

Connected through hyperedge HE-6 (induced by source paper "an experimental comparison of numerical and qualitative probabilistic reasoning"):
  with members:
    - "search based methods to bound diagnostic probabilities in very large belief nets"
    - "from conditional oughts to qualitative decision theory"

Connected through hyperedge HE-7 (induced by source paper "efficient enumeration of instantiations in bayesian networks"):
  with members:
    - "model based influence diagrams for machine vision"
    - "an efficient approach for finding the mpe in belief networks"

Connected through hyperedge HE-8 (induced by source paper "planning with external events"):
  with members:
    - "a method for planning given uncertain and incomplete information"

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)