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 Comprehensible Concept Representations from Trained Neural Networks"
  Abstract: Although they are applicable to a wide array of problems, and have demonstrated good performance on a number of difficult, real-world tasks, neural networks are not usually applied to problems in which comprehensibility of the acquired concepts is important. The concept representations formed by neural networks are hard to understand because they typically involve distributed, nonlinear relationships encoded by a large number of real-valued parameters. To address this limitation, we have been developing algorithms for extracting "symbolic" concept representations from trained neural networks. We first discuss why it is important to be able to understand the concept representations formed by neural networks. We then briefly describe our approach and discuss a number of issues pertaining to comprehensibility that have arisen in our work. Finally, we discuss choices that we have made in our research to date, and open research issues that we have not yet addressed.

Connected through hyperedge HE-1 (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 Tree-Structured Representations of Trained 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