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 Semantic Grammars with Constructive Inductive Logic Programming"
  Abstract: Automating the construction of semantic grammars is a difficult and interesting problem for machine learning. This paper shows how the semantic-grammar acquisition problem can be viewed as the learning of search-control heuristics in a logic program. Appropriate control rules are learned using a new first-order induction algorithm that automatically invents useful syntactic and semantic categories. Empirical results show that the learned parsers generalize well to novel sentences and out-perform previous approaches based on connectionist techniques.

Connected through hyperedge HE-1 (induced by source paper "Combining FOIL and EBG to Speed-up Logic Programs"):
  with members:
    - "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"
    - "Learning First-Order Definitions of Functions"

Connected through hyperedge HE-2 (induced by source paper "on Inductive Logic Programming (ILP-95) Inducing Logic Programs without Explicit Negative Examples"):
  with members:
    - "The Difficulties of Learning Logic Programs with Cut"
    - "Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models"

Connected through hyperedge HE-3 (induced by source paper "Combining Top-down and Bottom-up Techniques in Inductive Logic Programming"):
  with members:
    (none)

Connected through hyperedge HE-4 (induced by source paper "Computational Learning in Humans and Machines"):
  with members:
    - "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms"
    - "A Heuristic Approach to the Discovery of Macro-operators. Machine Learning, 3, 285-317. L e a"

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