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: "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.

Connected through hyperedge HE-1 (induced by source paper "Using Neural Networks to Automatically Refine Expert System Knowledge Bases: Experiments in the NYNEX MAX Domain"):
  with members:
    - "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees"
    - "Generating Accurate and Diverse Members of a Neural-Network Ensemble"
    - "Learning from Bad Data"

Connected through hyperedge HE-2 (induced by source paper "Submitted to the Future Generation Computer Systems special issue on Data Mining. Using Neural Networks"):
  with members:
    - "Extracting Provably Correct Rules from Artificial Neural Networks"
    - "Learning Symbolic Rules Using Artificial Neural Networks"
    - "Experiments with a New Boosting Algorithm"
    - "Extracting Comprehensible Concept Representations from Trained Neural Networks"

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: Neural_Networks