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: "on Inductive Logic Programming (ILP-95) Inducing Logic Programs without Explicit Negative Examples"
  Abstract: This paper presents a method for learning logic programs without explicit negative examples by exploiting an assumption of output completeness. A mode declaration is supplied for the target predicate and each training input is assumed to be accompanied by all of its legal outputs. Any other outputs generated by an incomplete program implicitly represent negative examples; however, large numbers of ground negative examples never need to be generated. This method has been incorporated into two ILP systems, Chillin and IFoil, both of which use intensional background knowledge. Tests on two natural language acquisition tasks, case-role mapping and past-tense learning, illustrate the advantages of the approach.

Connected through hyperedge HE-1 (induced by source paper "The Difficulties of Learning Logic Programs with Cut"):
  with members:
    - "The Challenge of Revising an Impure Theory"
    - "Learning Singly-Recursive Relations from Small Datasets"

Connected through hyperedge HE-2 (induced by source paper "Learning Semantic Grammars with Constructive Inductive Logic Programming"):
  with members:
    - "Combining Top-down and Bottom-up Techniques in Inductive Logic Programming"
    - "Computational Learning in Humans and Machines"
    - "Combining FOIL and EBG to Speed-up Logic Programs"

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:
    - "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"
    - "Learning First-Order Definitions of Functions"
    - "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