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 Testability of Causal Models with Latent and Instrumental Variables"
  Abstract: Certain causal models involving unmeasured variables induce no independence constraints among the observed variables but imply, nevertheless, inequality constraints on the observed distribution. This paper derives a general formula for such inequality constraints as induced by instrumental variables, that is, exogenous variables that directly affect some variables but not all. With the help of this formula, it is possible to test whether a model involving instrumental variables may account for the data, or, conversely, whether a given vari able can be deemed instrumental.

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"
    - "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"
    - "On The Foundation Of Structural Equation Models or"

Connected through hyperedge HE-2 (induced by source paper "Axioms of Causal Relevance"):
  with members:
    - "Probabilistic evaluation of sequential plans from causal models with hidden variables"

Connected through hyperedge HE-3 (induced by source paper "Probabilistic evaluation of sequential plans from causal models with hidden variables"):
  with members:
    - "The New Challenge: From a Century of Statistics to an Age of Causation"
    - "Causal diagrams for empirical research"
    - "Axioms of Causal Relevance"
    - "A Probabilistic Calculus of Actions"

Connected through hyperedge HE-4 (induced by source paper "FROM BAYESIAN NETWORKS TO CAUSAL NETWORKS"):
  with members:
    - ""Linear Dependencies Represented by Chain Graphs," "Graphical Modelling With MIM," Manual. "Identifying Independence in Bayesian"
    - "Causal diagrams for empirical research"
    - "Causal Inference from Indirect Experiments"
    - "A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task"
    - "group, and despite having just 337 subjects, the study strongly supports Identification of causal effects"

Connected through hyperedge HE-5 (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"
    - "A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task"
    - "Probabilistic evaluation of counterfactual queries"

Connected through hyperedge HE-6 (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"
    - "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"
    - "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