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: "Incremental Reduced Error Pruning"
  Abstract: This paper outlines some problems that may occur with Reduced Error Pruning in Inductive Logic Programming, most notably efficiency. Thereafter a new method, Incremental Reduced Error Pruning, is proposed that attempts to address all of these problems. Experiments show that in many noisy domains this method is much more efficient than alternative algorithms, along with a slight gain in accuracy. However, the experiments show as well that the use of this algorithm cannot be recommended for domains with a very specific concept description.

Connected through hyperedge HE-1 (induced by source paper "Transferring and Retraining Learned Information Filters"):
  with members:
    - "Learning Trees and Rules with Set-valued Features"
    - "Context-sensitive learning methods for text categorization"
    - "Feature Generation for Sequence Categorization"
    - "Is Learning The n-th Thing Any Easier Than Learning The First?"

Connected through hyperedge HE-2 (induced by source paper "More Efficient Windowing"):
  with members:
    - "Heterogeneous Uncertainty Sampling for Supervised Learning"

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