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: "Using the Grow-And-Prune Network to Solve Problems of Large Dimensionality"
  Abstract: This paper investigates a technique for creating sparsely connected feed-forward neural networks which may be capable of producing networks that have very large input and output layers. The architecture appears to be particularly suited to tasks that involve sparse training data as it is able to take advantage of the sparseness to further reduce training time. Some initial results are presented based on tests on the 16 bit compression problem.

Connected through hyperedge HE-1 (induced by source paper "The Free Speech  Phoneme Probability Estimation with Dynamic Sparsely Connected Artificial Neural Networks"):
  with members:
    - "Brief Papers Computing Second Derivatives in Feed-Forward Networks: A Review"

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