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 "representing and combining partially specified cpts"):
  Abstract: This paper extends previous work with network fragments and situation-specific network construction. We formally define the asymmetry network, an alternative representation for a conditional probability table. We also present an object-oriented representation for partially specified asymmetry networks. We show that the representation is parsimonious. We define an algebra for the elements of the representation that allows us to 'factor' any CPT and to soundly combine the partially specified asymmetry networks.

Includes members:
  Member "context specific approximation in probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning bayesian networks with restricted causal interactions")
  Member "elicitation of probabilities for belief networks combining qualitative and quantitative information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploiting qualitative knowledge in the learning of conditional probabilities of bayesian networks")
  Member "constructing situation specific belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hypothesis management in situation specific network construction")
  Member "learning bayesian networks with local structure":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a meta transfer objective for learning to disentangle causal mechanisms")
  Member "advances in probabilistic reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi context models for reasoning under partial knowledge generative process and inference grammar")
  Member "network fragments representing knowledge for constructing probabilistic models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bard a structured technique for group elicitation of bayesian networks to support analytic reasoning")
  Member "a generalization of the noisy or model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exploiting causality for selective belief filtering in dynamic bayesian networks")
  Member "context specific independence in bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "abstracting probabilistic models a logical perspective")

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)