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: "Learning Symbolic Rules Using Artificial Neural Networks"
  Abstract: A distinct advantage of symbolic learning algorithms over artificial neural networks is that typically the concept representations they form are more easily understood by humans. One approach to understanding the representations formed by neural networks is to extract symbolic rules from trained networks. In this paper we describe and investigate an approach for extracting rules from networks that uses (1) the NofM extraction algorithm, and (2) the network training method of soft weight-sharing. Previously, the NofM algorithm had been successfully applied only to knowledge-based neural networks. Our experiments demonstrate that our extracted rules generalize better than rules learned using the C4.5 system. In addition to being accurate, our extracted rules are also reasonably comprehensible.

Connected through hyperedge HE-1 (induced by source paper "Using Sampling and Queries to Extract Rules from Trained Neural Networks"):
  with members:
    - "The Effective Size of a Neural Network: A Principal Component Approach"
    - "Generalization and Exclusive Allocation of Credit in Unsupervised Category Learning"
    - "Extracting Provably Correct Rules from Artificial Neural Networks"

Connected through hyperedge HE-2 (induced by source paper "Machine learning in prognosis of the femoral neck fracture recovery examples, estimating attributes, explanation ability,"):
  with members:
    - "Estimating Attributes: Analysis and Extensions of RELIEF"
    - "Prognosing the Survival Time of the Patients with the Anaplastic Thyroid Carcinoma with Machine Learning"

Connected through hyperedge HE-3 (induced by source paper "Automatic Parameter Selection by Minimizing Estimated Error"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Feature Subset Selection as Search with Probabilistic Estimates"
    - "Machine Learning Methods for International Conflict Databases: A Case Study in Predicting Mediation Outcome"
    - "The Power of Decision Tables"
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"

Connected through hyperedge HE-4 (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"
    - "Experiments with a New Boosting Algorithm"
    - "Extracting Comprehensible Concept Representations from Trained Neural Networks"
    - "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