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: "dynamic construction of belief networks"
  Abstract: We describe a method for incrementally constructing belief networks. We have developed a network-construction language similar to a forward-chaining language using data dependencies, but with additional features for specifying distributions. Using this language, we can define parameterized classes of probabilistic models. These parameterized models make it possible to apply probabilistic reasoning to problems for which it is impractical to have a single large static model.

Connected through hyperedge HE-1 (induced by source paper "the complexity of bayesian networks specified by propositional and relational languages"):
  with members:
    - "on the semantics and complexity of probabilistic logic programs"
    - "new liftable classes for first order probabilistic inference"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "lower complexity bounds for lifted inference"
    - "tractability through exchangeability a new perspective on efficient probabilistic inference"
    - "understanding the complexity of lifted inference and asymmetric weighted model counting"
    - "query dags a practical paradigm for implementing belief network inference"
    - "a simple fptas for counting edge covers"

Connected through hyperedge HE-2 (induced by source paper "lazy factored inference for functional probabilistic programming"):
  with members:
    - "pruning bayesian networks for efficient computation"
    - "generating bayesian networks from probability logic knowledge bases"
    - "expectation propagation for approximate bayesian inference"
    - "church a language for generative models"
    - "an empirical evaluation of possible variations of lazy propagation"

Connected through hyperedge HE-3 (induced by source paper "a graph theoretic analysis of information value"):
  with members:
    - "reasoning about the value of decision model refinement methods and application"
    - "reformulating inference problems through selective conditioning"
    - "efficient decision theoretic planning techniques and empirical analysis"
    - "ideal reformulation 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-4 (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"
    - "ideal a software package for analysis of influence diagrams"

Connected through hyperedge HE-5 (induced by source paper "probabilistic conceptual network a belief representation scheme for utility based categorization"):
  with members:
    - "reasoning about the value of decision model refinement methods and application"
    - "problem formulation as the reduction of a decision model"
    - "representing context sensitive knowledge in a network formalism a preliminary report"

Connected through hyperedge HE-6 (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"
    - "ideal a software package for analysis of influence diagrams"
    - "representing bayesian networks within probabilistic horn abduction"

Connected through hyperedge HE-7 (induced by source paper "graph grammar assistance for automated generation of influence diagrams"):
  with members:
    - "decision making using probabilistic inference methods"
    - "problem formulation as the reduction of a decision model"
    - "representing context sensitive knowledge in a network formalism a preliminary report"

Connected through hyperedge HE-8 (induced by source paper "representation requirements for supporting decision model formulation"):
  with members:
    - "integrating logical and probabilistic reasoning for decision making"

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)