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 "Using Sampling and Queries to Extract Rules from Trained Neural Networks"):
  Abstract: Concepts learned by neural networks are difficult to understand because they are represented using large assemblages of real-valued parameters. One approach to understanding trained neural networks is to extract symbolic rules that describe their classification behavior. There are several existing rule-extraction approaches that operate by searching for such rules. We present a novel method that casts rule extraction not as a search problem, but instead as a learning problem. In addition to learning from training examples, our method exploits the property that networks can be efficiently queried. We describe algorithms for extracting both conjunctive and M -of-N rules, and present experiments that show that our method is more efficient than conventional search-based approaches.

Includes members:
  Member "The Effective Size of a Neural Network: A Principal Component Approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "A Practical Bayesian Framework for Backprop Networks")
  Member "Generalization and Exclusive Allocation of Credit in Unsupervised Category Learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Rearrangement of receptive field topography after intracortical and peripheral stimulation: The role of plasticity in")
  Member "Extracting Provably Correct Rules from Artificial Neural Networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Constructing Fuzzy Graphs from Examples")
  Member "Learning Symbolic Rules Using Artificial Neural Networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Machine learning in prognosis of the femoral neck fracture recovery examples, estimating attributes, explanation ability,")

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