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 with restricted causal interactions"):
  Abstract: A major problem for the learning of Bayesian networks (BNs) is the exponential number of parameters needed for conditional probability tables. Recent research reduces this complexity by modeling local structure in the probability tables. We examine the use of log-linear local models. While log-linear models in this context are not new (Whittaker, 1990; Buntine, 1991; Neal, 1992; Heckerman and Meek, 1997), for structure learning they are generally subsumed under a naive Bayes model. We describe an alternative interpretation, and use a Minimum Message Length (MML) (Wallace, 1987) metric for structure learning of networks exhibiting causal independence, which we term first-order networks (FONs). We also investigate local model selection on a node-by-node basis.

Includes members:
  Member "context specific approximation in probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "representing and combining partially specified cpts")
  Member "bayesian learning of loglinear models for neural connectivity":
    also connected through other hyperedges:
      (none)
  Member "structure and parameter learning for causal independence and causal interaction models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning the structure of dynamic probabilistic networks")
  Member "parameter adjustment in bayes networks the generalized noisy or gate":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepfense online accelerated defense against adversarial deep learning")
  Member "inter causal independence and heterogeneous factorization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inference with causal independence in the cpsc network")
  Member "context specific independence in bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "abstracting probabilistic models a logical perspective")
  Member "theory refinement on bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a tutorial on learning with bayesian 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)