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 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.

Connected through hyperedge HE-1 (induced by source paper "On the Sample Complexity of Learning Bayesian Networks"):
  with members:
    - "A Tutorial on Learning With Bayesian Networks"

Connected through hyperedge HE-2 (induced by source paper "Context-Specific Independence in Bayesian Networks"):
  with members:
    - "BUCKET ELIMINATION: A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE"
    - "Exploiting Causal Independence in Bayesian Network Inference"
    - "Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks"
    - "Structured Representation of Complex Stochastic Systems"
    - "The Frame Problem and Bayesian Network Action Representations"
    - "A Constraint-Based Approach to Preference Elicitation and Decision Making"

Connected through hyperedge HE-3 (induced by source paper "Sequential Update of Bayesian Network Structure"):
  with members:
    - "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS"
    - "A Tutorial on Learning With Bayesian Networks"
    - "Learning Belief Networks in the Presence of Missing Values and Hidden Variables"
    - "Generalized Prioritized Sweeping"

Connected through hyperedge HE-4 (induced by source paper "Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks"):
  with members:
    - "Context-Specific Independence in Bayesian Networks"
    - "The Frame Problem and Bayesian Network Action Representations"
    - "Dynamic Belief Networks for Discrete Monitoring"
    - "Stochastic simulation algorithms for dynamic probabilistic networks"

Connected through hyperedge HE-5 (induced by source paper "A Tutorial on Learning With Bayesian Networks"):
  with members:
    - "Learning Bayesian Networks from Incomplete Data"
    - "Sequential Update of Bayesian Network Structure"
    - "Automated Decomposition of Model-based Learning Problems"
    - "Bayesian Finite Mixtures for Nonlinear Modeling of Educational data"
    - "On the Sample Complexity of Learning Bayesian Networks"
    - "Update rules for parameter estimation in Bayesian networks"
    - "Discovering Structure in Continuous Variables Using Bayesian Networks"
    - "Learning Belief Networks in the Presence of Missing Values and Hidden Variables"

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