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: "integrating model construction and evaluation"
  Abstract: To date, most probabilistic reasoning systems have relied on a fixed belief network constructed at design time. The network is used by an application program as a representation of (in)dependencies in the domain. Probabilistic inference algorithms operate over the network to answer queries. Recognizing the inflexibility of fixed models has led researchers to develop automated network construction procedures that use an expressive knowledge base to generate a network that can answer a query. Although more flexible than fixed model approaches, these construction procedures separate construction and evaluation into distinct phases. In this paper we develop an approach to combining incremental construction and evaluation of a partial probability model. The combined method holds promise for improved methods for control of model construction based on a trade-off between fidelity of results and cost of construction.

Connected through hyperedge HE-1 (induced by source paper "constructing situation specific belief networks"):
  with members:
    - "pruning bayesian networks for efficient computation"
    - "a theoretical framework for context sensitive temporal probability model construction with application to plan projection"
    - "generating bayesian networks from probability logic knowledge bases"
    - "network fragments representing knowledge for constructing probabilistic models"
    - "d separation from theorems to algorithms"
    - "tradeoffs in constructing and evaluating temporal influence diagrams"
    - "computational advantages of relevance reasoning in bayesian belief networks"

Connected through hyperedge HE-2 (induced by source paper "reasoning about the value of decision model refinement methods and application"):
  with members:
    - "refinement and coarsening of bayesian networks"
    - "ideal a software package for analysis of influence diagrams"
    - "reasoning about beliefs and actions under computational resource constraints"

Connected through hyperedge HE-3 (induced by source paper "tradeoffs in constructing and evaluating temporal influence diagrams"):
  with members:
    - "ideal reformulation of belief networks"
    - "dynamic network models for forecasting"

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)