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 "hypothesis management in situation specific network construction"):
  Abstract: This paper considers the problem of knowledge-based model construction in the presence of uncertainty about the association of domain entities to random variables. Multi-entity Bayesian networks (MEBNs) are defined as a representation for knowledge in domains characterized by uncertainty in the number of relevant entities, their interrelationships, and their association with observables. An MEBN implicitly specifies a probability distribution in terms of a hierarchically structured collection of Bayesian network fragments that together encode a joint probability distribution over arbitrarily many interrelated hypotheses. Although a finite query-complete model can always be constructed, association uncertainty typically makes exact model construction and evaluation intractable. The objective of hypothesis management is to balance tractability against accuracy. We describe an application to the problem of using intelligence reports to infer the organization and activities of groups of military vehicles. Our approach is compared to related work in the tracking and fusion literature.

Includes members:
  Member "constructing situation specific belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "representing and combining partially specified cpts")
  Member "generating bayesian networks from probability logic knowledge bases":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "survey on models and techniques for root cause analysis")
  Member "network fragments representing knowledge for constructing probabilistic models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bard a structured technique for group elicitation of bayesian networks to support analytic reasoning")
  Member "a probabilistic analysis of marker passing techniques for plan recognition":
    also connected through other hyperedges:
      (none)
  Member "object oriented bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "probabilistic software modeling a data driven paradigm for software analysis")
  Member "spook a system for probabilistic object oriented knowledge representation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hinge loss markov random fields and probabilistic soft logic")
  Member "computational advantages of relevance reasoning in bayesian belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "constructing situation specific belief networks")

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)