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: "Correlated Action Effects in Decision Theoretic Regression"
  Abstract: Much recent research in decision theoretic planning has adopted Markov decision processes (MDPs) as the model of choice, and has attempted to make their solution more tractable by exploiting problem structure. One particular algorithm, structured policy construction achieves this by means of a decision theoretic analog of goal regression, using action descriptions based on Bayesian networks with tree-structured conditional probability tables. The algorithm as presented is not able to deal with actions with correlated effects. We describe a new decision theoretic regression operator that corrects this weakness. While conceptually straightforward, this extension requires a somewhat more complicated technical approach.

Connected through hyperedge HE-1 (induced by source paper "Structured Reachability Analysis for Markov Decision Processes"):
  with members:
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "The Frame Problem and Bayesian Network Action Representations"
    - "Approximating Value Trees in Structured Dynamic Programming"

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