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: ""Linear Dependencies Represented by Chain Graphs," "Graphical Modelling With MIM," Manual. "Identifying Independence in Bayesian"
  Abstract: 8] Dori, D. and Tarsi, M., "A Simple Algorithm to Construct a Consistent Extension of a Partially Oriented Graph," Computer Science Department, Tel-Aviv University. Also Technical Report R-185, UCLA, Cognitive Systems Laboratory, October 1992. [14] Pearl, J. and Wermuth, N., "When Can Association Graphs Admit a Causal Interpretation?," UCLA, Cognitive Systems Laboratory, Technical Report R-183-L, November 1992. [17] Verma, T.S. and Pearl, J., "Deciding Morality of Graphs is NP-complete," Technical Report R-188, UCLA, Cognitive Systems Laboratory, October 1992.

Connected through hyperedge HE-1 (induced by source paper "Bayesian inference for nondecomposable graphical Gaussian models"):
  with members:
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"

Connected through hyperedge HE-2 (induced by source paper "An Alternative Markov Property for Chain Graphs"):
  with members:
    - "On the Markov Equivalence of Chain Graphs, Undirected Graphs, and Acyclic Digraphs"

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

Connected through hyperedge HE-4 (induced by source paper "ASPECTS OF GRAPHICAL MODELS CONNECTED WITH CAUSALITY"):
  with members:
    - "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"
    - "Probabilistic evaluation of counterfactual queries"

Connected through hyperedge HE-5 (induced by source paper "Bayesian Graphical Models for Discrete Data"):
  with members:
    - "Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models"
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"
    - "Accounting for Model Uncertainty in Survival Analysis Improves Predictive Performance"
    - "Model Selection for Generalized Linear Models via GLIB, with Application to Epidemiology 1"
    - "Markov Chain Monte Carlo Model Determination for Hierarchical and Graphical Log-linear Models"
    - "Decomposable graphical Gaussian model determination"
    - "Statistical Ideas for Selecting Network Architectures"
    - "Bayesian Graphical Modeling for Intelligent Tutoring Systems"

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