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 Belief Networks from Data: An Information Theory Based Approach"):
  Abstract: This paper presents an efficient algorithm for learning Bayesian belief networks from databases. The algorithm takes a database as input and constructs the belief network structure as output. The construction process is based on the computation of mutual information of attribute pairs. Given a data set that is large enough, this algorithm can generate a belief network very close to the underlying model, and at the same time, enjoys the time When the data set has a normal DAG-Faithful (see Section 3.2) probability distribution, the algorithm guarantees that the structure of a perfect map [Pearl, 1988] of the underlying dependency model is generated. To evaluate this algorithm, we present the experimental results on three versions of the well-known ALARM network database, which has 37 attributes and 10,000 records. The results show that this algorithm is accurate and efficient. The proof of correctness and the analysis of complexity of O N( ) 4 on conditional independence (CI) tests.

Includes members:
  Member "A guide to the literature on learning probabilistic networks from data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "An Algorithm for Bayesian Belief Network Construction from Data")
  Member "An Algorithm for the Construction of Bayesian Network Structures from Data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management")
  Member "An Algorithm for Bayesian Belief Network Construction from Data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "A guide to the literature on learning probabilistic networks from data")

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