human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent groups of papers sharing a common citing paper.

Details of the hypergraph:
Center node: "An Algorithm for Bayesian Belief Network Construction from Data"
  Abstract: This paper presents an efficient algorithm for constructing Bayesian belief networks from databases. The algorithm takes a database and an attributes ordering (i.e., the causal attributes of an attribute should appear earlier in the order) as input and constructs a belief network structure as output. The construction process is based on the computation of mutual information of attribute pairs. Given a data set which is large enough and has a DAG-Isomorphic probability distribution, this algorithm guarantees that the perfect map [1] of the underlying dependency tests. 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 correctness proof and the analysis of computational complexity are also presented. We also discuss the features of our work and relate it to previous works. model is generated, and at the same time, enjoys the time complexity of O N( ) 2 on conditional independence (CI)

Connected through hyperedge HE-1 (induced by source paper "Learning Belief Networks from Data: An Information Theory Based Approach"):
  with members:
    - "A guide to the literature on learning probabilistic networks from data"
    - "An Algorithm for the Construction of Bayesian Network Structures from Data"

Connected through hyperedge HE-2 (induced by source paper "A guide to the literature on learning probabilistic networks from data"):
  with members:
    - "Learning Belief Networks from Data: An Information Theory Based Approach"

Connected through hyperedge HE-3 (induced by source paper "An Algorithm for the Construction of Bayesian Network Structures from Data"):
  with members:
    - "Learning Bayesian Networks from Incomplete Data"
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"
    - "A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task"
    - "Efficient Learning of Selective Bayesian Network Classifiers"
    - "Learning Belief Networks from Data: An Information Theory Based Approach"
    - "Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management"

Question: Please tell me which class the center node 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