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 Strategies for the MTiling Constructive Learning Algorithm"
  Abstract: We present a framework for incorporating pruning strategies in the MTiling constructive neural network learning algorithm. Pruning involves elimination of redundant elements (connection weights or neurons) from a network and is of considerable practical interest. We describe three elementary sensitivity based strategies for pruning neurons. Experimental results demonstrate a moderate to significant reduction in the network size without compromising the network's generalization performance.

Connected through hyperedge HE-1 (induced by source paper "Generative Learning Structures and Processes for Generalized Connectionist Networks"):
  with members:
    - "Symbolic and Subsymbolic Learning for Vision: Some Possibilities"
    - "Properties of Genetic Representations of Neural Architectures"
    - "Some Biases for Efficient Learning of Spatial, Temporal, and Spatio-Temporal Patterns"
    - "Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms"
    - "Faster Learning in Multi-Layer Networks by Handling"
    - "A Simple Randomized Quantization Algorithm for Neural Network Pattern Classifiers"

Connected through hyperedge HE-2 (induced by source paper "Coordination and Control Structures and Processes: Possibilities for Connectionist Networks (CN)"):
  with members:
    - "BRAINSTRUCTURED CONNECTIONIST NETWORKS THAT PERCEIVE AND LEARN"
    - "Experiments with the Cascade-Correlation Algorithm"
    - "Perceptual Development and Learning: From Behavioral, Neurophysiological, and Morphological Evidence To Computational Models"
    - "Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms"
    - "A Simple Randomized Quantization Algorithm for Neural Network Pattern Classifiers"

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