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: "Counterfactuals and Policy Analysis in Structural Models"
  Abstract: Evaluation of counterfactual queries (e.g., "If A were true, would C have been true?") is important to fault diagnosis, planning, determination of liability, and policy analysis. We present a method for evaluating counter-factuals when the underlying causal model is represented by structural models a nonlinear generalization of the simultaneous equations models commonly used in econometrics and social sciences. This new method provides a coherent means for evaluating policies involving the control of variables which, prior to enacting the policy were influenced by other variables in the system.

Connected through hyperedge HE-1 (induced by source paper "A Probabilistic Calculus of Actions"):
  with members:
    - "Probabilistic evaluation of sequential plans from causal models with hidden 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 "Probabilistic evaluation of counterfactual queries"):
  with members:
    - "ASPECTS OF GRAPHICAL MODELS CONNECTED WITH CAUSALITY"
    - "A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task"
    - "Decision-Theoretic Foundations for Causal Reasoning"

Connected through hyperedge HE-3 (induced by source paper "On The Foundation Of Structural Equation Models or"):
  with members:
    - "Causal diagrams for empirical research"

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