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: "Defining Explanation in Probabilistic Systems"
  Abstract: As probabilistic systems gain popularity and are coming into wider use, the need for a mechanism that explains the system's findings and recommendations becomes more critical. The system will also need a mechanism for ordering competing explanations. We examine two representative approaches to explanation in the literature one due to G ardenfors and one due to Pearland show that both suffer from significant problems. We propose an approach to defining a notion of better explanation that combines some of the features of both together with more recent work by Pearl and others on causality.

Connected through hyperedge HE-1 (induced by source paper "Causal diagrams for empirical research"):
  with members:
    - "The New Challenge: From a Century of Statistics to an Age of Causation"
    - "Causal Inference from Indirect Experiments"
    - "Probabilistic evaluation of sequential plans from causal models with hidden variables"
    - "On the Testability of Causal Models with Latent and Instrumental Variables"
    - "FROM BAYESIAN NETWORKS TO CAUSAL NETWORKS"
    - "Decision-Theoretic Foundations for Causal Reasoning"
    - "UNIVERSAL FORMULAS FOR TREATMENT EFFECTS FROM NONCOMPLIANCE DATA"
    - "On The Foundation Of Structural Equation Models or"

Connected through hyperedge HE-2 (induced by source paper "Explaining Predictions in Bayesian Networks and Influence Diagrams"):
  with members:
    - "Abduction as Belief Revision"

Connected through hyperedge HE-3 (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"
    - "Explaining Predictions in Bayesian Networks and Influence Diagrams"

Connected through hyperedge HE-4 (induced by source paper "Decision-Theoretic Foundations for Causal Reasoning"):
  with members:
    - "Causal diagrams for empirical research"
    - "A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task"
    - "Probabilistic evaluation of counterfactual queries"

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