human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "LEARNING BAYESIAN NETWORKS WITH LOCAL STRUCTURE"):
  Abstract: We examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly represents and learns the local structure in the conditional probability distributions (CPDs) that quantify these networks. This increases the space of possible models, enabling the representation of CPDs with a variable number of parameters. The resulting learning procedure induces models that better emulate the interactions present in the data. We describe the theoretical foundations and practical aspects of learning local structures and provide an empirical evaluation of the proposed learning procedure. This evaluation indicates that learning curves characterizing this procedure converge faster, in the number of training instances, than those of the standard procedure, which ignores the local structure of the CPDs. Our results also show that networks learned with local structures tend to be more complex (in terms of arcs), yet require fewer parameters.

Includes members:
  Member "Context-Specific Independence in Bayesian Networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "The Frame Problem and Bayesian Network Action Representations")
  Member "Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Context-Specific Independence in Bayesian Networks")
  Member "Sequential Update of Bayesian Network Structure":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Learning Belief Networks in the Presence of Missing Values and Hidden Variables")
  Member "A Tutorial on Learning With Bayesian Networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Update rules for parameter estimation in Bayesian networks")
  Member "On the Sample Complexity of Learning Bayesian Networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "A Tutorial on Learning With Bayesian Networks")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) belongs to. The 7 classes are: Neural_Networks, Reinforcement_Learning, Probabilistic_Methods, Theory, Genetic_Algorithms, Case_Based, Rule_Learning. Directly output the class name.

gpt: Probabilistic_Methods