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: "Producing More Comprehensible Models While Retaining Their Performance"
  Abstract: Rissanen's Minimum Description Length (MDL) principle is adapted to handle continuous attributes in the Inductive Logic Programming setting. Application of the developed coding as a MDL pruning mechanism is devised. The behavior of the MDL pruning is tested in a synthetic domain with artificially added noise of different levels and in two real life problems | modelling of the surface roughness of a grinding workpiece and modelling of the mutagenicity of nitroaromatic compounds. Results indicate that MDL pruning is a successful parameter-free noise fighting tool in real-life domains since it acts as a safeguard against building too complex models while retaining the accuracy of the model.

Connected through hyperedge HE-1 (induced by source paper "First Order Regression: Applications in Real-World Domains"):
  with members:
    - "First Order Regression"

Connected through hyperedge HE-2 (induced by source paper "First Order Regression"):
  with members:
    - "Employing Linear Regression in Regression Tree Leaves"
    - "First Order Regression: Applications in Real-World Domains"

Connected through hyperedge HE-3 (induced by source paper "Employing Linear Regression in Regression Tree Leaves"):
  with members:
    - "Structural Regression Trees"
    - "An adaptation of Relief for attribute estimation in regression"
    - "First Order Regression"
    - "Prognosing the Survival Time of the Patients with the Anaplastic Thyroid Carcinoma with Machine Learning"
    - "Context-sensitive attribute estimation in regression"
    - "The Bayesian Approach to Tree-Structured Regression"

Connected through hyperedge HE-4 (induced by source paper "Stochastic search in inductive concept learning"):
  with members:
    - "Rule Induction with CN2: Some Recent Improvements"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "An investigation of noise-tolerant relational concept learning algorithms"
    - "Linear Space Induction in First Order Logic with RELIEFF"
    - "SFOIL: Stochastic Approach to Inductive Logic Programming"
    - "An application of ILP in a musical database: learning to compose the two-voice counterpoint"

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