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: "Submitted to the Future Generation Computer Systems special issue on Data Mining. Using Neural Networks"
  Abstract: Neural networks have been successfully applied in a wide range of supervised and unsupervised learning applications. Neural-network methods are not commonly used for data-mining tasks, however, because they often produce incomprehensible models and require long training times. In this article, we describe neural-network learning algorithms that are able to produce comprehensible models, and that do not require excessive training times. Specifically, we discuss two classes of approaches for data mining with neural networks. The first type of approach, often called rule extraction, involves extracting symbolic models from trained neural networks. The second approach is to directly learn simple, easy-to-understand networks. We argue that, given the current state of the art, neural-network methods deserve a place in the tool boxes of data-mining specialists.

Connected through hyperedge HE-1 (induced by source paper "Extracting Comprehensible Concept Representations from Trained Neural Networks"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Extracting Provably Correct Rules from Artificial Neural Networks"):
  with members:
    - "Using Sampling and Queries to Extract Rules from Trained Neural Networks"
    - "Constructing Fuzzy Graphs from Examples"

Connected through hyperedge HE-3 (induced by source paper "Extracting Tree-Structured Representations of Trained Networks"):
  with members:
    - "Using Neural Networks to Automatically Refine Expert System Knowledge Bases: Experiments in the NYNEX MAX Domain"

Connected through hyperedge HE-4 (induced by source paper "Experiments with a New Boosting Algorithm"):
  with members:
    - "Improving Bagging Performance by Increasing Decision Tree Diversity"
    - "Boosting Trees for Cost-Sensitive Classifications"
    - "A Method of Combining Multiple Probabilistic Classifiers through Soft Competition on Different Feature Sets"
    - "Pruning Adaptive Boosting ICML-97 Final Draft"
    - "Adaptive Boosting of Neural Networks for Character Recognition"
    - "Achieving High-Accuracy Text-to-Speech with Machine Learning"
    - "An Empirical Evaluation of Bagging and Boosting"
    - "Why Does Bagging Work? A Bayesian Account and its Implications bagging's success, both in a"

Connected through hyperedge HE-5 (induced by source paper "Learning Symbolic Rules Using Artificial Neural Networks"):
  with members:
    - "Automatic Parameter Selection by Minimizing Estimated Error"
    - "Using Sampling and Queries to Extract Rules from Trained Neural Networks"
    - "Machine learning in prognosis of the femoral neck fracture recovery examples, estimating attributes, explanation ability,"

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