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: "Some Experiments with Real-time Decision Algorithms"
  Abstract: Real-time Decision algorithms are a class of incremental resource-bounded [Horvitz, 89] or anytime [Dean, 93] algorithms for evaluating influence diagrams. We present a test domain for real-time decision algorithms, and the results of experiments with several Real-time Decision Algorithms in this domain. The results demonstrate high performance for two algorithms, a decision-evaluation variant of Incremental Probabilisitic Inference [DAmbrosio, 93] and a variant of an algorithm suggested by Goldszmidt, [Goldszmidt, 95], PK-reduced. We discuss the implications of these experimental results and explore the broader applicability of these algorithms.

Connected through hyperedge HE-1 (induced by source paper "Efficient Inference in Bayes Networks As A Combinatorial Optimization Problem"):
  with members:
    - "Case-Based Probability Factoring in Bayesian Belief Networks"
    - "Sensitivities: An Alternative to Conditional Probabilities for Bayesian Belief Networks"
    - "Algebraic Techniques for Efficient Inference in Bayesian Networks"
    - "Interpretation of Complex Scenes Using Bayesian Networks"

Connected through hyperedge HE-2 (induced by source paper "Learning policies for partially observable environments: Scaling up"):
  with members:
    - "A Formal Framework for Speedup Learning from Problems and Solutions"
    - "Acting under Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation"
    - "Approximating Optimal Policies for Partially Observable Stochastic Domains"
    - "Learning sorting and decision trees with POMDPs"
    - "Incremental methods for computing bounds in partially observable Markov decision processes"
    - "Efficient dynamic-programming updates in partially observable Markov decision processes"

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