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 engineering for complex belief networks"
  Abstract: Like any large system development effort, the construction of a complex belief network model requires systems engineering to manage the design and construction process. We propose a rapid prototyping approach to network engineering. We describe criteria for identifying network modules and the use of "stubs" to represent not-yet-constructed modules. We propose an object oriented representation for belief networks which captures the semantics of the problem in addition to conditional independencies and probabilities. Methods for evaluating complex belief network models are discussed. The ideas are illustrated with examples from a large belief network construction problem in the military intelligence domain.

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 "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"
    - "network fragments representing knowledge for constructing probabilistic models"
    - "a generalization of the noisy or model"
    - "context specific independence in bayesian networks"

Connected through hyperedge HE-3 (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 fragments representing knowledge for constructing probabilistic models"

Connected through hyperedge HE-4 (induced by source paper "spook a system for probabilistic object oriented knowledge representation"):
  with members:
    - "network fragments representing knowledge for constructing probabilistic models"
    - "context specific independence in bayesian 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)