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 diagrams for empirical research"
  Abstract: The primary aim of this paper is to show how graphical models can be used as a mathematical language for integrating statistical and subject-matter information. In particular, the paper develops a principled, nonparametric framework for causal inference, in which diagrams are queried to determine if the assumptions available are sufficient for identifying causal effects from nonexperimental data. If so the diagrams can be queried to produce mathematical expressions for causal effects in terms of observed distributions; otherwise, the diagrams can be queried to suggest additional observations or auxiliary experiments from which the desired inferences can be obtained. Key words: Causal inference, graph models, structural equations, treatment effect.

Connected through hyperedge HE-1 (induced by source paper "Causal Inference from Indirect Experiments"):
  with members:
    - "The New Challenge: From a Century of Statistics to an Age of Causation"
    - "FROM BAYESIAN NETWORKS TO CAUSAL NETWORKS"
    - "group, and despite having just 337 subjects, the study strongly supports Identification of causal effects"

Connected through hyperedge HE-2 (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"
    - "Axioms of Causal Relevance"
    - "A Probabilistic Calculus of Actions"
    - "On the Testability of Causal Models with Latent and Instrumental Variables"

Connected through hyperedge HE-3 (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 Inference from Indirect Experiments"
    - "On the Testability of Causal Models with Latent and Instrumental Variables"
    - "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-4 (induced by source paper "The New Challenge: From a Century of Statistics to an Age of Causation"):
  with members:
    - "Causal Inference from Indirect Experiments"
    - "Probabilistic evaluation of sequential plans from causal models with hidden variables"

Connected through hyperedge HE-5 (induced by source paper "UNIVERSAL FORMULAS FOR TREATMENT EFFECTS FROM NONCOMPLIANCE DATA"):
  with members:
    - "Causal inference, path analysis, and recursive struc-tural equations models. In C. Clogg, editor, Sociological Methodology,"

Connected through hyperedge HE-6 (induced by source paper "On The Foundation Of Structural Equation Models or"):
  with members:
    - "Counterfactuals and Policy Analysis in Structural Models"

Connected through hyperedge HE-7 (induced by source paper "Decision-Theoretic Foundations for Causal Reasoning"):
  with members:
    - "A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task"
    - "Probabilistic evaluation of counterfactual queries"
    - "Defining Explanation in Probabilistic Systems"

Connected through hyperedge HE-8 (induced by source paper "On the Testability of Causal Models with Latent and Instrumental Variables"):
  with members:
    - "Probabilistic evaluation of sequential plans from causal models with hidden variables"
    - "Axioms of Causal Relevance"
    - "ASPECTS OF GRAPHICAL MODELS CONNECTED WITH CAUSALITY"
    - "A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task"
    - "FROM BAYESIAN NETWORKS TO CAUSAL NETWORKS"

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