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: "Stochastic search in inductive concept learning"
  Abstract: Concept learning can be viewed as search of the space of concept descriptions. The hypothesis language determines the search space. In standard inductive learning algorithms, the structure of the search space is determined by generalization/specialization operators. Algorithms perform locally optimal search by using a hill-climbing and/or a beam-search strategy. To overcome this limitation, concept learning can be viewed as stochastic search of the space of concept descriptions. The proposed stochastic search method is based on simulated annealing which is known as a successful means for solving combinatorial optimization problems. The stochastic search method, implemented in a rule learning system ATRIS, is based on a compact and efficient representation of the problem and the appropriate operators for structuring the search space. Furthermore, by heuristic pruning of the search space, the method enables also handling of imperfect data. The paper introduces the stochastic search method, describes the ATRIS learning algorithm and gives results of the experiments.

Connected through hyperedge HE-1 (induced by source paper "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"):
  with members:
    - "Pessimistic decision tree pruning based on tree size"
    - "Multivariate Decision Trees"
    - "Dynamic Automatic Model Selection"
    - "Finding new rules for incomplete theories: Explicit biases for induction with contextual information. In Proceedings"
    - "Induction of Oblique Decision Trees"
    - "Data Analyses Using Simulated Breeding and Inductive Learning Methods"
    - "Learning Decision Trees from Decision Rules:"
    - "The Estimation of Probabilities in Attribute Selection Measures for Decision Tree Induction"

Connected through hyperedge HE-2 (induced by source paper "SFOIL: Stochastic Approach to Inductive Logic Programming"):
  with members:
    - "Naive Bayesian classifier within ILP-R"
    - "Linear Space Induction in First Order Logic with RELIEFF"
    - "An application of ILP in a musical database: learning to compose the two-voice counterpoint"
    - "Overcoming the myopia of inductive learning algorithms with RELIEFF"
    - "Stochastic Propositionalization of Non-Determinate Background Knowledge"

Connected through hyperedge HE-3 (induced by source paper "Rule Induction with CN2: Some Recent Improvements"):
  with members:
    - "Stochastically Guided Disjunctive Version Space Learning"
    - "Using Qualitative Models to Guide Inductive Learning"
    - "Case-Based Sonogram Classification"
    - "Relating Relational Learning Algorithms"
    - "Generalizing from Case Studies: A Case Study"
    - "Incremental Reduced Error Pruning"
    - "What do Constructive Learners Really Learn?"
    - "Unsupervised Constructive Learning"

Connected through hyperedge HE-4 (induced by source paper "An investigation of noise-tolerant relational concept learning algorithms"):
  with members:
    - "Utilizing Prior Concepts for Learning"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Incremental Reduced Error Pruning"
    - "Fossil: A Robust Relational Learner"
    - "A Comparison of Pruning Methods for Relational Concept Learning"
    - "Top-Down Pruning in Relational Learning"

Connected through hyperedge HE-5 (induced by source paper "An application of ILP in a musical database: learning to compose the two-voice counterpoint"):
  with members:
    - "Naive Bayesian classifier within ILP-R"
    - "Linear Space Induction in First Order Logic with RELIEFF"
    - "SFOIL: Stochastic Approach to Inductive Logic Programming"

Connected through hyperedge HE-6 (induced by source paper "Linear Space Induction in First Order Logic with RELIEFF"):
  with members:
    - "Estimating Attributes: Analysis and Extensions of RELIEF"
    - "Naive Bayesian classifier within ILP-R"
    - "SFOIL: Stochastic Approach to Inductive Logic Programming"
    - "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-7 (induced by source paper "Producing More Comprehensible Models While Retaining Their Performance"):
  with members:
    - "Employing Linear Regression in Regression Tree Leaves"
    - "First Order Regression"
    - "First Order Regression: Applications in Real-World Domains"

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