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 "GENERALIZATION BY CONTROLLED EXPANSION OF EXAMPLES"):
  Abstract: SG (Specific to General) is a learning system that derives general rules from specific examples. SG learns incrementally with good speed and generalization. The SG network is built of many simple nodes that adapt to the problem being learned. Learning is done without requiring user adjustment of sensitive parameters and noise is tolerated with graceful degradation in performance. Nodes learn important features in the input space and then monitor the ability of the features to predict output values. Learning is O(n log n) for each example, where n is the number of nodes in the network, and the number of inputs and output values are treated as constants. An enhanced network topology reduces time complexity to O(log n). Empirical results show that the model gives good generalization and that learning converges in a small number of training passes.

Includes members:
  Member "Efficient Construction of Networks for Learned Representations with General to Specific Relationships":
    also connected through other hyperedges:
      (none)

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: Neural_Networks