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: "group, and despite having just 337 subjects, the study strongly supports Identification of causal effects"
  Abstract: Figure 8a and Figure 8b show the prior distribution over f(-CR ) that follows from the flat prior and the skewed prior, respectively. Figure 8c and Figure 8d show the posterior distribution p(f (-CR jD)) obtained by our system when run on the Lipid data, using the flat prior and the skewed prior, respectively. From the bounds of Balke and Pearl (1994), it follows that under the large-sample assumption, 0:51 f (-CR jD) 0:86. Figure 8: Prior (a, b) and posterior (c,d) distributions for a subpopulation f (-CR jD) specified by the counter-factual query "Would Joe have improved had he taken the drug, given that he did not improve without it". (a) corresponds to the flat prior, (b) to the skewed prior. This paper identifies and demonstrates a new application area for network-based inference techniques - the management of causal analysis in clinical experimentation. These techniques, which were originally developed for medical diagnosis, are shown capable of circumventing one of the major problems in clinical experiments the assessment of treatment efficacy in the face of imperfect compliance. While standard diagnosis involves purely probabilistic inference in fully specified networks, causal analysis involves partially specified networks in which the links are given causal interpretation and where the domain of some variables are unknown. The system presented in this paper provides the clinical research community, we believe for the first time, an assumption-free, unbiased assessment of the average treatment effect. We offer this system as a practical tool to be used whenever full compliance cannot be enforced and, more broadly, whenever the data available is insufficient for answering the queries of interest to the clinical investigator. Lipid Research Clinic Program. 1984. The lipid research clinics coronary primary prevention trial results, parts i and ii. Journal of the American Medical Association 251(3):351-374. January.

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"
    - "Causal diagrams for empirical research"
    - "FROM BAYESIAN NETWORKS TO CAUSAL NETWORKS"

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"
    - "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"

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