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 Provably Correct Rules from Artificial Neural Networks"
  Abstract: Although connectionist learning procedures have been applied successfully to a variety of real-world scenarios, artificial neural networks have often been criticized for exhibiting a low degree of comprehensibility. Mechanisms that automatically compile neural networks into symbolic rules offer a promising perspective to overcome this practical shortcoming of neural network representations. This paper describes an approach to neural network rule extraction based on Validity Interval Analysis (VI-Analysis). VI-Analysis is a generic tool for extracting symbolic knowledge from Backpropagation-style artificial neural networks. It does this by propagating whole intervals of activations through the network in both the forward and backward directions. In the context of rule extraction, these intervals are used to prove or disprove the correctness of conjectured rules. We describe techniques for generating and testing rule hypotheses, and demonstrate these using some simple classification tasks including the MONK's benchmark problems. Rules extracted by VI-Analysis are provably correct. No assumptions are made about the topology of the network at hand, as well as the procedure employed for training the network.

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"
    - "Learning Symbolic Rules Using Artificial Neural Networks"

Connected through hyperedge HE-2 (induced by source paper "Constructing Fuzzy Graphs from Examples"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "Submitted to the Future Generation Computer Systems special issue on Data Mining. Using Neural Networks"):
  with members:
    - "Learning Symbolic Rules Using 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