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: "An Experimental Comparison of Genetic Programming and Inductive Logic Programming on Learning Recursive List Functions"
  Abstract: This paper experimentally compares three approaches to program induction: inductive logic programming (ILP), genetic programming (GP), and genetic logic programming (GLP) (a variant of GP for inducing Pro-log programs). Each of these methods was used to induce four simple, recursive, list-manipulation functions. The results indicate that ILP is the most likely to induce a correct program from small sets of random examples, while GP is generally less accurate. GLP performs the worst, and is rarely able to induce a correct program. Interpretations of these results in terms of differences in search methods and inductive biases are presented. Keywords: Genetic Programming, Inductive Logic Programming, Empiri cal Comparison This paper will also be submitted to the 8th Int. Workshop on Inductive Logic Programming, 1998.

Connected through hyperedge HE-1 (induced by source paper "Advantages of Decision Lists and Implicit Negatives in Inductive Logic Programming"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Learning First-Order Definitions of Functions"):
  with members:
    - "Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models"
    - "Combining FOIL and EBG to Speed-up Logic Programs"

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