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: "Two Algorithms for Inducing Structural Equation Models from Data"
  Abstract: We present two algorithms for inducing structural equation models from data. Assuming no latent variables, these models have a causal interpretation and their parameters may be estimated by linear multiple regression. Our algorithms are comparable with PC [15] and IC [12, 11], which rely on conditional independence. We present the algorithms and empirical comparisons with PC and IC.

Connected through hyperedge HE-1 (induced by source paper "Causal inference, path analysis, and recursive struc-tural equations models. In C. Clogg, editor, Sociological Methodology,"):
  with members:
    - "A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task"
    - "Regression Can Build Predictive Causal Models"
    - "UNIVERSAL FORMULAS FOR TREATMENT EFFECTS FROM NONCOMPLIANCE DATA"

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"
    - "Regression Can Build Predictive Causal Models"

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"
    - "Causal inference, path analysis, and recursive struc-tural equations models. In C. Clogg, editor, Sociological Methodology,"
    - "A Statistical Semantics for Causation Key words: causality, induction, learning"

Connected through hyperedge HE-4 (induced by source paper "A Statistical Semantics for Causation Key words: causality, induction, learning"):
  with members:
    - "Regression Can Build Predictive Causal Models"

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