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: "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.

Connected through hyperedge HE-1 (induced by source paper "bard a structured technique for group elicitation of bayesian networks to support analytic reasoning"):
  with members:
    - "formalizing neurath s ship approximate algorithms for online causal learning"
    - "strategies for generating micro explanations for bayesian belief networks"
    - "explanation of probabilistic inference for decision support systems"

Connected through hyperedge HE-2 (induced by source paper "representing and combining partially specified cpts"):
  with members:
    - "context specific approximation in probabilistic inference"
    - "elicitation of probabilities for belief networks combining qualitative and quantitative information"
    - "constructing situation specific belief networks"
    - "learning bayesian networks with local structure"
    - "advances in probabilistic reasoning"
    - "a generalization of the noisy or model"
    - "context specific independence in bayesian networks"
    - "computing upper and lower bounds on likelihoods in intractable networks"

Connected through hyperedge HE-3 (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"
    - "d separation from theorems to algorithms"
    - "integrating model construction and evaluation"
    - "tradeoffs in constructing and evaluating temporal influence diagrams"
    - "computational advantages of relevance reasoning in bayesian belief networks"

Connected through hyperedge HE-4 (induced by source paper "hypothesis management in situation specific network construction"):
  with members:
    - "constructing situation specific belief networks"
    - "generating bayesian networks from probability logic knowledge bases"
    - "a probabilistic analysis of marker passing techniques for plan recognition"
    - "object oriented bayesian networks"
    - "spook a system for probabilistic object oriented knowledge representation"
    - "computational advantages of relevance reasoning in bayesian belief networks"

Connected through hyperedge HE-5 (induced by source paper "causal mechanism based model construction"):
  with members:
    - "causality in bayesian belief networks"
    - "a bayesian method for constructing bayesian belief networks from databases"
    - "a generalization of the noisy or model"
    - "object oriented bayesian networks"
    - "spook a system for probabilistic object oriented knowledge representation"

Connected through hyperedge HE-6 (induced by source paper "toward general analysis of recursive probability models"):
  with members:
    - "approximations in bayesian belief universe for knowledge based systems"
    - "context specific independence in bayesian networks"
    - "object oriented bayesian networks"
    - "spook a system for probabilistic object oriented knowledge representation"

Connected through hyperedge HE-7 (induced by source paper "object oriented bayesian networks"):
  with members:
    - "a theoretical framework for context sensitive temporal probability model construction with application to plan projection"
    - "optimization of inter subnet belief updating in multiply sectioned bayesian networks"
    - "a probabilistic approach to hierarchical model based diagnosis"
    - "network engineering for complex belief networks"

Connected through hyperedge HE-8 (induced by source paper "spook a system for probabilistic object oriented knowledge representation"):
  with members:
    - "context specific independence in bayesian networks"
    - "network engineering for complex belief networks"
    - "object oriented bayesian networks"

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)