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: "Linear Space Induction in First Order Logic with RELIEFF"
  Abstract: Current ILP algorithms typically use variants and extensions of the greedy search. This prevents them to detect significant relationships between the training objects. Instead of myopic impurity functions, we propose the use of the heuristic based on RELIEF for guidance of ILP algorithms. At each step, in our ILP-R system, this heuristic is used to determine a beam of candidate literals. The beam is then used in an exhaustive search for a potentially good conjunction of literals. From the efficiency point of view we introduce interesting declarative bias which enables us to keep the growth of the training set, when introducing new variables, within linear bounds (linear with respect to the clause length). This bias prohibits cross-referencing of variables in variable dependency tree. The resulting system has been tested on various artificial problems. The advantages and deficiencies of our approach are discussed.

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"
    - "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-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"
    - "SFOIL: Stochastic Approach to Inductive Logic Programming"

Connected through hyperedge HE-3 (induced by source paper "Naive Bayesian classifier within ILP-R"):
  with members:
    - "Estimating Attributes: Analysis and Extensions of RELIEF"
    - "SFOIL: Stochastic Approach to Inductive Logic Programming"
    - "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"
    - "SFOIL: Stochastic Approach to Inductive Logic Programming"

Connected through hyperedge HE-5 (induced by source paper "Discretization of continuous attributes using ReliefF"):
  with members:
    - "Estimating Attributes: Analysis and Extensions of RELIEF"
    - "Induction of decision trees using 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"
    - "SFOIL: Stochastic Approach to Inductive Logic Programming"
    - "An application of ILP in a musical database: learning to compose the two-voice counterpoint"

Connected through hyperedge HE-7 (induced by source paper "Estimating Attributes: Analysis and Extensions of RELIEF"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Feature Subset Selection as Search with Probabilistic Estimates"
    - "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"
    - "Induction of decision trees and Bayesian classification applied to diagnosis of sport injuries"
    - "Machine learning in prognosis of the femoral neck fracture recovery examples, estimating attributes, explanation ability,"
    - "Naive Bayesian classifier within ILP-R"

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