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: "More Efficient Windowing"
  Abstract: Windowing has been proposed as a procedure for efficient memory use in the ID3 decision tree learning algorithm. However, previous work has shown that windowing may often lead to a decrease in performance. In this work, we try to argue that separate-and-conquer rule learning algorithms are more appropriate for windowing than divide-and-conquer algorithms, because they learn rules independently and are less susceptible to changes in class distributions. In particular, we will present a new windowing algorithm that achieves additional gains in efficiency by exploiting this property of separate-and-conquer algorithms. While the presented algorithm is only suitable for redundant, noise-free data sets, we will also briefly discuss the problem of noisy data in windowing and present some preliminary ideas how it might be solved with an extension of the algorithm introduced in this paper.

Connected through hyperedge HE-1 (induced by source paper "Incremental Reduced Error Pruning"):
  with members:
    - "Transferring and Retraining Learned Information Filters"

Connected through hyperedge HE-2 (induced by source paper "Heterogeneous Uncertainty Sampling for Supervised Learning"):
  with members:
    - "Information-based objective functions for active data selection"
    - "Learning Trees and Rules with Set-valued Features"
    - "Context-sensitive learning methods for text categorization"
    - "Query by Committee"

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: Rule_Learning