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: "Integrity Constraints in ILP using a Monte Carlo approach"
  Abstract: Many state-of-the-art ILP systems require large numbers of negative examples to avoid overgeneralization. This is a considerable disadvantage for many ILP applications, namely indu ctive program synthesis where relativelly small and sparse example sets are a more realistic scenario. Integrity constraints are first order clauses that can play the role of negative examples in an inductive process. One integrity constraint can replace a long list of ground negative examples. However, checking the consistency of a program with a set of integrity constraints usually involves heavy the orem-proving. We propose an efficient constraint satisfaction algorithm that applies to a wide variety of useful integrity constraints and uses a Monte Carlo strategy. It looks for inconsistencies by ra ndom generation of queries to the program. This method allows the use of integrity constraints instead of (or together with) negative examples. As a consequence programs to induce can be specified more rapidly by the user and the ILP system tends to obtain more accurate definitions. Average running times are not greatly affected by the use of integrity constraints compared to ground negative examples.

Connected through hyperedge HE-1 (induced by source paper "Architecture for Iterative Learning of Recursive Definitions"):
  with members:
    - "INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning"
    - "Learning by Refining Algorithm Sketches"

Connected through hyperedge HE-2 (induced by source paper "Learning by Refining Algorithm Sketches"):
  with members:
    - "Learning Recursion with Iterative Bootstrap Induction (Extended Abstract)"
    - "Architecture for Iterative Learning of Recursive Definitions"

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