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: "Explaining Predictions in Bayesian Networks and Influence Diagrams"
  Abstract: As Bayesian Networks and Influence Diagrams are being used more and more widely, the importance of an efficient explanation mechanism becomes more apparent. We focus on predictive explanations, the ones designed to explain predictions and recommendations of probabilistic systems. We analyze the issues involved in defining, computing and evaluating such explanations and present an algorithm to compute them.

Connected through hyperedge HE-1 (induced by source paper "Abduction as Belief Revision"):
  with members:
    - "Generalized Update: Belief Change in Dynamic Settings"
    - "Rank-based systems: A simple approach to belief revision, belief update, and reasoning about evidence and actions."
    - "Abduction to Plausible Causes: An Event-based Model of Belief Update"
    - "Defining Explanation in Probabilistic Systems"

Connected through hyperedge HE-2 (induced by source paper "Defining Explanation in Probabilistic Systems"):
  with members:
    - "Causal diagrams for empirical research"
    - "Abduction as Belief Revision"
    - "Decision-Theoretic Foundations for Causal Reasoning"

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