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 from Indirect Experiments"
  Abstract: Indirect experiments are studies in which randomized control is replaced by randomized encouragement, that is, subjects are encouraged, rather than forced to receive treatment programs. The purpose of this paper is to bring to the attention of experimental researchers simple mathematical results that enable us to assess, from indirect experiments, the strength with which causal influences operate among variables of interest. The results reveal that despite the laxity of the encouraging instrument, indirect experimentation can yield significant and sometimes accurate information on the impact of a program on the population as a whole, as well as on the particular individuals who participated in the program.

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"
    - "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 "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"
    - "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-3 (induced by source paper "group, and despite having just 337 subjects, the study strongly supports Identification of causal effects"):
  with members:
    - "FROM BAYESIAN NETWORKS TO CAUSAL NETWORKS"

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 diagrams for empirical research"
    - "Probabilistic evaluation of sequential plans from causal models with hidden 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