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: "ILP with Noise and Fixed Example Size: A Bayesian Approach"
  Abstract: Current inductive logic programming systems are limited in their handling of noise, as they employ a greedy covering approach to constructing the hypothesis one clause at a time. This approach also causes difficulty in learning recursive predicates. Additionally, many current systems have an implicit expectation that the cardinality of the positive and negative examples reflect the "proportion" of the concept to the instance space. A framework for learning from noisy data and fixed example size is presented. A Bayesian heuristic for finding the most probable hypothesis in this general framework is derived. This approach evaluates a hypothesis as a whole rather than one clause at a time. The heuristic, which has nice theoretical properties, is incorporated in an ILP system, Lime. Experimental results show that Lime handles noise better than FOIL and PROGOL. It is able to learn recursive definitions from noisy data on which other systems do not perform well. Lime is also capable of learning from only positive data and also from only negative data.

Connected through hyperedge HE-1 (induced by source paper "Extraction of Meta-Knowledge to Restrict the Hypothesis Space for ILP Systems incorporating them in FOIL."):
  with members:
    - "Inverse entailment and Progol"

Connected through hyperedge HE-2 (induced by source paper "Learning from positive data"):
  with members:
    (none)

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