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: "The New Challenge: From a Century of Statistics to an Age of Causation"
  Abstract: Some of the main users of statistical methods - economists, social scientists, and epidemiologists are discovering that their fields rest not on statistical but on causal foundations. The blurring of these foundations over the years follows from the lack of mathematical notation capable of distinguishing causal from equational relationships. By providing formal and natural explication of such relations, graphical methods have the potential to revolutionize how statistics is used in knowledge-rich applications. Statisticians, in response, are beginning to realize that causality is not a metaphysical dead-end but a meaningful concept with clear mathematical underpinning. The paper surveys these developments and outlines future challenges.

Connected through hyperedge HE-1 (induced by source paper "Causal diagrams for empirical research"):
  with members:
    - "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"
    - "On The Foundation Of Structural Equation Models or"

Connected through hyperedge HE-2 (induced by source paper "Causal Inference from Indirect Experiments"):
  with members:
    - "Causal diagrams for empirical research"
    - "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-3 (induced by source paper "Probabilistic evaluation of sequential plans from causal models with hidden variables"):
  with members:
    - "Causal diagrams for empirical research"
    - "Axioms of Causal Relevance"
    - "A Probabilistic Calculus of Actions"
    - "On the Testability of Causal Models with Latent and Instrumental Variables"

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