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 "A Tutorial on Learning With Bayesian Networks"):
  Abstract: Technical Report MSR-TR-95-06

Includes members:
  Member "LEARNING BAYESIAN NETWORKS WITH LOCAL STRUCTURE":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "On the Sample Complexity of Learning Bayesian Networks")
  Member "Learning Bayesian Networks from Incomplete Data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "An Algorithm for the Construction of Bayesian Network Structures from Data")
  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 "Automated Decomposition of Model-based Learning Problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide")
  Member "Bayesian Finite Mixtures for Nonlinear Modeling of Educational data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Comparing Bayesian Model Class Selection Criteria by Discrete Finite Mixtures")
  Member "On the Sample Complexity of Learning Bayesian Networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "LEARNING BAYESIAN NETWORKS WITH LOCAL STRUCTURE")
  Member "Update rules for parameter estimation in Bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide")
  Member "Discovering Structure in Continuous Variables Using Bayesian Networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide")

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