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: "Probabilistic evaluation of counterfactual queries"
  Abstract: To appear in the Twelfth National Conference on Artificial Intelligence (AAAI-94), Seattle, WA, July 31 August 4, 1994. Technical Report R-213-A April, 1994 Abstract Evaluation of counterfactual queries (e.g., "If A were true, would C have been true?") is important to fault diagnosis, planning, and determination of liability. We present a formalism that uses probabilistic causal networks to evaluate one's belief that the counterfactual consequent, C, would have been true if the antecedent, A, were true. The antecedent of the query is interpreted as an external action that forces the proposition A to be true, which is consistent with Lewis' Miraculous Analysis. This formalism offers a concrete embodiment of the "closest world" approach which (1) properly reflects common understanding of causal influences, (2) deals with the uncertainties inherent in the world, and (3) is amenable to machine representation.

Connected through hyperedge HE-1 (induced by source paper "ASPECTS OF GRAPHICAL MODELS CONNECTED WITH CAUSALITY"):
  with members:
    - ""Linear Dependencies Represented by Chain Graphs," "Graphical Modelling With MIM," Manual. "Identifying Independence in Bayesian"
    - "On the Testability of Causal Models with Latent and Instrumental Variables"
    - "A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task"

Connected through hyperedge HE-2 (induced by source paper "Counterfactuals and Policy Analysis in Structural Models"):
  with members:
    - "A Probabilistic Calculus of Actions"
    - "On The Foundation Of Structural Equation Models or"

Connected through hyperedge HE-3 (induced by source paper "A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task"):
  with members:
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"
    - "A Probabilistic Calculus of Actions"
    - "On the Testability of Causal Models with Latent and Instrumental Variables"
    - "ASPECTS OF GRAPHICAL MODELS CONNECTED WITH CAUSALITY"
    - "FROM BAYESIAN NETWORKS TO CAUSAL NETWORKS"
    - "MDL Learning of Probabilistic Neural Networks for Discrete Problem Domains"
    - "Two Algorithms for Inducing Structural Equation Models from Data"
    - "Regression Can Build Predictive Causal Models"

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"
    - "Defining Explanation in Probabilistic Systems"

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