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: "Causal inference, path analysis, and recursive struc-tural equations models. In C. Clogg, editor, Sociological Methodology,"
  Abstract: Lipid Research Clinic Program 84] Lipid Research Clinic Program. The Lipid Research Clinics Coronary Primary Prevention Trial results, parts I and II. Journal of the American Medical Association, 251(3):351-374, January 1984. [Pearl 93] Judea Pearl. Aspects of graphical models connected with causality. Technical Report R-195-LL, Cognitive Systems Laboratory, UCLA, June 1993. Submitted to Biometrika (June 1993). Short version in Proceedings of the 49th Session of the International Statistical Institute: Invited papers, Flo rence, Italy, August 1993, Tome LV, Book 1, pp. 391-401.

Connected through hyperedge HE-1 (induced by source paper "Two Algorithms for Inducing Structural Equation Models from Data"):
  with members:
    - "A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task"
    - "Regression Can Build Predictive Causal Models"
    - "A Statistical Semantics for Causation Key words: causality, induction, learning"

Connected through hyperedge HE-2 (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"
    - "Probabilistic evaluation of counterfactual queries"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "Regression Can Build Predictive Causal Models"):
  with members:
    - "A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task"
    - "Two Algorithms for Inducing Structural Equation Models from Data"
    - "A Statistical Semantics for Causation Key words: causality, induction, learning"

Connected through hyperedge HE-4 (induced by source paper "UNIVERSAL FORMULAS FOR TREATMENT EFFECTS FROM NONCOMPLIANCE DATA"):
  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