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 "learning bayesian networks the combination of knowledge and statistical data"):
  Abstract: We describe algorithms for learning Bayesian networks from a combination of user knowledge and statistical data. The algorithms have two components: a scoring metric and a search procedure. The scoring metric takes a network structure, statistical data, and a user's prior knowledge, and returns a score proportional to the posterior probability of the network structure given the data. The search procedure generates networks for evaluation by the scoring metric. Our contributions are threefold. First, we identify two important properties of metrics, which we call event equivalence and parameter modularity. These properties have been mostly ignored, but when combined, greatly simplify the encoding of a user's prior knowledge. In particular, a user can express her knowledge-for the most part-as a single prior Bayesian network for the domain. Second, we describe local search and annealing algorithms to be used in conjunction with scoring metrics. In the special case where each node has at most one parent, we show that heuristic search can be replaced with a polynomial algorithm to identify the networks with the highest score. Third, we describe a methodology for evaluating Bayesian-network learning algorithms. We apply this approach to a comparison of metrics and search procedures.

Includes members:
  Member "causality in bayesian belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "managing large scale scientific hypotheses as uncertain and probabilistic data")
  Member "a definition and graphical representation for causality":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "identifying independencies in causal graphs with feedback")
  Member "an algorithm for the construction of bayesian network structures from data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "causal inference in the presence of latent variables and selection bias")
  Member "a transformational characterization of equivalent bayesian network structures":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a tutorial on learning with bayesian networks")
  Member "a construction of bayesian networks from databases based on an mdl principle":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bayesian learning of sum product networks")
  Member "using causal information and local measures to learn bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "asymptotic model selection for directed networks with hidden variables")
  Member "local expression languages for probabilistic dependence a preliminary report":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fast belief update using order of magnitude probabilities")
  Member "deriving a minimal i map of a belief network relative to a target ordering of its nodes":
    also connected through other hyperedges:
      (none)

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)