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 "fast belief update using order of magnitude probabilities"):
  Abstract: We present an algorithm, called Predict, for updating beliefs in causal networks quantified with order-of-magnitude probabilities. The algorithm takes advantage of both the structure and the quantification of the network and presents a polynomial asymptotic complexity. Predict exhibits a conservative behavior in that it is always sound but not always complete. We provide sufficient conditions for completeness and present algorithms for testing these conditions and for computing a complete set of plausible values. We propose Predict as an efficient method to estimate probabilistic values and illustrate its use in conjunction with two known algorithms for probabilistic inference. Finally, we describe an application of Predict to plan evaluation, present experimental results, and discuss issues regarding its use with conditional logics of belief, and in the characterization of irrelevance.

Includes members:
  Member "search based methods to bound diagnostic probabilities in very large belief nets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "real time top k predictive query processing over event streams")
  Member "approximations in bayesian belief universe for knowledge based systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a tutorial on learning with bayesian networks")
  Member "parallel belief revision":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the relation between kappa calculus and probabilistic reasoning")
  Member "the use of conflicts in searching bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "independence of causal influence and clique tree propagation")
  Member "bounded conditioning flexible inference for decisions under scarce resources":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using qualitative relationships for bounding probability distributions")
  Member "local expression languages for probabilistic dependence a preliminary report":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning bayesian networks the combination of knowledge and statistical 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)