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: "SFOIL: Stochastic Approach to Inductive Logic Programming"
  Abstract: Current systems in the field of Inductive Logic Programming (ILP) use, primarily for the sake of efficiency, heuristically guided search techniques. Such greedy algorithms suffer from local optimization problem. Present paper describes a system named SFOIL, that tries to alleviate this problem by using a stochastic search method, based on a generalization of simulated annealing, called Markovian neural network. Various tests were performed on benchmark, and real-world domains. The results show both, advantages and weaknesses of stochastic approach.

Connected through hyperedge HE-1 (induced by source paper "Stochastic Propositionalization of Non-Determinate Background Knowledge"):
  with members:
    - "Theory and Applications of Agnostic PAC-Learning with Small Decision Trees"
    - "Learning Trees and Rules with Set-valued Features"
    - "Inverse entailment and Progol"

Connected through hyperedge HE-2 (induced by source paper "Overcoming the myopia of inductive learning algorithms with RELIEFF"):
  with members:
    - "Estimating Attributes: Analysis and Extensions of RELIEF"
    - "An adaptation of Relief for attribute estimation in regression"
    - "Prognosing the Survival Time of the Patients with the Anaplastic Thyroid Carcinoma with Machine Learning"
    - "Context-sensitive attribute estimation in regression"
    - "Linear Space Induction in First Order Logic with RELIEFF"

Connected through hyperedge HE-3 (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"
    - "An application of ILP in a musical database: learning to compose the two-voice counterpoint"

Connected through hyperedge HE-4 (induced by source paper "An application of ILP in a musical database: learning to compose the two-voice counterpoint"):
  with members:
    - "Stochastic search in inductive concept learning"
    - "Naive Bayesian classifier within ILP-R"
    - "Linear Space Induction in First Order Logic with RELIEFF"

Connected through hyperedge HE-5 (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"
    - "An application of ILP in a musical database: learning to compose the two-voice counterpoint"
    - "Discretization of continuous attributes using ReliefF"
    - "Overcoming the myopia of inductive learning algorithms with RELIEFF"

Connected through hyperedge HE-6 (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"
    - "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