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: "An application of ILP in a musical database: learning to compose the two-voice counterpoint"
  Abstract: We describe SFOIL, a descendant of FOIL that uses the advanced stochastic search heuristic, and its application in learning to compose the two-voice counterpoint. The application required learning a 4-ary relation from more than 20.000 training instances. SFOIL is able to efficiently deal with this learning task which is to our knowledge one of the most complex learning task solved by an ILP system. This demonstrates that ILP systems can scale up to real databases and that top-down ILP systems that use the covering approach and advanced search strategies are appropriate for knowledge discovery in databases and are promising for further investigation.

Connected through hyperedge HE-1 (induced by source paper "SFOIL: Stochastic Approach to Inductive Logic Programming"):
  with members:
    - "Stochastic search in inductive concept learning"
    - "Naive Bayesian classifier within ILP-R"
    - "Linear Space Induction in First Order Logic with RELIEFF"
    - "Overcoming the myopia of inductive learning algorithms with RELIEFF"
    - "Stochastic Propositionalization of Non-Determinate Background Knowledge"

Connected through hyperedge HE-2 (induced by source paper "Naive Bayesian classifier within ILP-R"):
  with members:
    - "Estimating Attributes: Analysis and Extensions of RELIEF"
    - "Linear Space Induction in First Order Logic with RELIEFF"
    - "SFOIL: Stochastic Approach to Inductive Logic Programming"

Connected through hyperedge HE-3 (induced by source paper "Linear Space Induction in First Order Logic with RELIEFF"):
  with members:
    - "Estimating Attributes: Analysis and Extensions of RELIEF"
    - "Stochastic search in inductive concept learning"
    - "Naive Bayesian classifier within ILP-R"
    - "SFOIL: Stochastic Approach to Inductive Logic Programming"
    - "Discretization of continuous attributes using ReliefF"
    - "Overcoming the myopia of inductive learning algorithms with RELIEFF"

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"
    - "Producing More Comprehensible Models While Retaining Their Performance"
    - "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"

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