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: "On The Foundation Of Structural Equation Models or"
  Abstract: When Can We Give Causal Interpretation Abstract The assumptions underlying statistical estimation are of fundamentally different character from the causal assumptions that underly structural equation models (SEM). The differences have been blurred through the years for the lack of a mathematical notation capable of distinguishing causal from equational relationships. Recent advances in graphical methods provide formal explication of these differences, and are destined to have profound impact on SEM's practice and philosophy.

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"
    - "Defining Explanation in Probabilistic Systems"
    - "FROM BAYESIAN NETWORKS TO CAUSAL NETWORKS"
    - "Decision-Theoretic Foundations for Causal Reasoning"
    - "UNIVERSAL FORMULAS FOR TREATMENT EFFECTS FROM NONCOMPLIANCE DATA"

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