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: "Pruning Recurrent Neural Networks for Improved Generalization Performance"
  Abstract: Determining the architecture of a neural network is an important issue for any learning task. For recurrent neural networks no general methods exist that permit the estimation of the number of layers of hidden neurons, the size of layers or the number of weights. We present a simple pruning heuristic which significantly improves the generalization performance of trained recurrent networks. We illustrate this heuristic by training a fully recurrent neural network on positive and negative strings of a regular grammar. We also show that if rules are extracted from networks trained to recognize these strings, that rules extracted after pruning are more consistent with the rules to be learned. This performance improvement is obtained by pruning and retraining the networks. Simulations are shown for training and pruning a recurrent neural net on strings generated by two regular grammars, a randomly-generated 10-state grammar and an 8-state triple parity grammar. Further simulations indicate that this pruning method can gives generalization performance superior to that obtained by training with weight decay.

Connected through hyperedge HE-1 (induced by source paper "Using Recurrent Neural Networks to Learn the Structure of Interconnection Networks"):
  with members:
    - "Performance of On-Line Learning Methods in Predicting Multiprocessor Memory Access Patterns"
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "First-Order vs. Second-Order Single Layer Recurrent Neural Networks"
    - "A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction"

Connected through hyperedge HE-2 (induced by source paper "Extraction of Rules from Discrete-Time Recurrent Neural Networks"):
  with members:
    - "Rule Revision with Recurrent Neural Networks"
    - "Noisy Time Series Prediction using Symbolic Representation and Recurrent Neural Network Grammatical Inference"
    - "Analysis of Dynamical Recognizers"

Connected through hyperedge HE-3 (induced by source paper "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks"):
  with members:
    - "Actively Searching for an Effective Neural-Network Ensemble"
    - "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms"
    - "Generating Accurate and Diverse Members of a Neural-Network Ensemble"
    - "An Empirical Evaluation of Bagging and Boosting"

Connected through hyperedge HE-4 (induced by source paper "Evolving Artificial Neural Networks using the Baldwin Effect"):
  with members:
    - "Growing Cell Structures A Self-organizing Network for Unsupervised and Supervised Learning"

Connected through hyperedge HE-5 (induced by source paper "A Delay Damage Model Selection Algorithm for NARX Neural Networks"):
  with members:
    - "Learning networks for face analysis and synthesis"
    - "PREDICTING SUNSPOTS AND EXCHANGE RATES WITH CONNECTIONIST NETWORKS"
    - "Neural Learning of Chaotic Dynamics: The Error Propagation Algorithm trains a neural network to identify"

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