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: "Learning First-Order Definitions of Functions"
  Abstract: First-order learning involves finding a clause-form definition of a relation from examples of the relation and relevant background information. In this paper, a particular first-order learning system is modified to customize it for finding definitions of functional relations. This restriction leads to faster learning times and, in some cases, to definitions that have higher predictive accuracy. Other first-order learning systems might benefit from similar specialization.

Connected through hyperedge HE-1 (induced by source paper "Combining FOIL and EBG to Speed-up Logic Programs"):
  with members:
    - "Learning Semantic Grammars with Constructive Inductive Logic Programming"
    - "Learning Goal-Decomposition Rules using Exercises"
    - "Theory-guided Empirical Speedup Learning of Goal Decomposition Rules"
    - "Learning Approximate Control Rules Of High Utility"
    - "An intelligent search method using Inductive Logic Programming"

Connected through hyperedge HE-2 (induced by source paper "An Experimental Comparison of Genetic Programming and Inductive Logic Programming on Learning Recursive List Functions"):
  with members:
    - "Advantages of Decision Lists and Implicit Negatives in Inductive Logic Programming"

Connected through hyperedge HE-3 (induced by source paper "Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models"):
  with members:
    - "on Inductive Logic Programming (ILP-95) Inducing Logic Programs without Explicit Negative Examples"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"
    - "Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs"
    - "Memory-Based Lexical Acquisition and Processing"
    - "Inverse entailment and Progol"

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