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: "The Free Speech  Phoneme Probability Estimation with Dynamic Sparsely Connected Artificial Neural Networks"
  Abstract: This paper presents new methods for training large neural networks for phoneme probability estimation. An architecture combining timedelay windows and recurrent connections is used to capture the important dynamic information of the speech signal. Because the number of connections in a fully connected recurrent network grows super-linear with the number of hidden units, schemes for sparse connection and connection pruning are explored. It is found that sparsely connected networks outperform their fully connected counterparts with an equal number of connections. The implementation of the combined architecture and training scheme is described in detail. The networks are evaluated in a hybrid HMM/ANN system for phoneme recognition on the TIMIT database, and for word recognition on the WAXHOLM database. The achieved phone error-rate, 27.8%, for the standard 39 phoneme set on the core testset of the TIMIT database is in the range of the lowest reported. All training and simulation software used is made freely available by the author, and detailed information about the software and the training process is given in an Appendix.

Connected through hyperedge HE-1 (induced by source paper "Brief Papers Computing Second Derivatives in Feed-Forward Networks: A Review"):
  with members:
    - "A Practical Bayesian Framework for Backprop Networks"
    - "A comparison of some error estimates for neural network models  Summary"

Connected through hyperedge HE-2 (induced by source paper "Using the Grow-And-Prune Network to Solve Problems of Large Dimensionality"):
  with members:
    (none)

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