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 "Extracting Tree-Structured Representations of Trained Networks"):
  Abstract: A significant limitation of neural networks is that the representations they learn are usually incomprehensible to humans. We present a novel algorithm, Trepan, for extracting comprehensible, symbolic representations from trained neural networks. Our algorithm uses queries to induce a decision tree that approximates the concept represented by a given network. Our experiments demonstrate that Trepan is able to produce decision trees that maintain a high level of fidelity to their respective networks while being comprehensible and accurate. Unlike previous work in this area, our algorithm is general in its applicability and scales well to large net works and problems with high-dimensional input spaces.

Includes members:
  Member "Submitted to the Future Generation Computer Systems special issue on Data Mining. Using Neural Networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Extracting Comprehensible Concept Representations from Trained Neural Networks")
  Member "Using Neural Networks to Automatically Refine Expert System Knowledge Bases: Experiments in the NYNEX MAX Domain":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Learning from Bad Data")

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