human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "network fragments representing knowledge for constructing probabilistic models"):
  Abstract: In most current applications of belief networks, domain knowledge is represented by a single belief network that applies to all problem instances in the domain. In more complex domains, problem-specific models must be constructed from a knowledge base encoding probabilistic relationships in the domain. Most work in knowledge-based model construction takes the rule as the basic unit of knowledge. We present a knowledge representation framework that permits the knowledge base designer to specify knowledge in larger semantically meaningful units which we call network fragments. Our framework provides for representation of asymmetric independence and canonical intercausal interaction. We discuss the combination of network fragments to form problem-specific models to reason about particular problem instances. The framework is illustrated using examples from the domain of military situation awareness.

Includes members:
  Member "a theoretical framework for context sensitive temporal probability model construction with application to plan projection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lower complexity bounds for lifted inference")
  Member "causal independence for knowledge acquisition and inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploiting causality for selective belief filtering in dynamic bayesian networks")
  Member "model based influence diagrams for machine vision":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dynamic network updating techniques for diagnostic reasoning")
  Member "advances in probabilistic reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi context models for reasoning under partial knowledge generative process and inference grammar")
  Member "context specific independence in bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "abstracting probabilistic models a logical perspective")
  Member "knowledge engineering for large belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "independence of causal influence and clique tree propagation")
  Member "object oriented bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistic software modeling a data driven paradigm for software analysis")
  Member "utility based control for computer vision":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a decision theoretic model for using scientific data")

Question: Please tell me which class the source paper 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)