human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Estimating Attributes: Analysis and Extensions of RELIEF":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Induction of decision trees and Bayesian classification applied to diagnosis of sport injuries")
  Member "Stochastic search in inductive concept learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine")
  Member "Naive Bayesian classifier within ILP-R":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "SFOIL: Stochastic Approach to Inductive Logic Programming")
  Member "SFOIL: Stochastic Approach to Inductive Logic Programming":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Stochastic Propositionalization of Non-Determinate Background Knowledge")
  Member "An application of ILP in a musical database: learning to compose the two-voice counterpoint":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "SFOIL: Stochastic Approach to Inductive Logic Programming")
  Member "Discretization of continuous attributes using ReliefF":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Induction of decision trees using RELIEFF")
  Member "Overcoming the myopia of inductive learning algorithms with RELIEFF":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "SFOIL: Stochastic Approach to Inductive Logic Programming")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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