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: "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.

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

Connected through hyperedge HE-2 (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"
    - "An Algorithm for Bayesian Belief Network Construction from Data"
    - "Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management"

Connected through hyperedge HE-3 (induced by source paper "An Algorithm for Bayesian Belief Network Construction from Data"):
  with members:
    - "A guide to the literature on learning probabilistic networks from data"
    - "An Algorithm for the Construction of Bayesian Network Structures from Data"

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