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: "Neural Network Implementation in SAS R Software"
  Abstract: The estimation or training methods in the neural network literature are usually some simple form of gradient descent algorithm suitable for implementation in hardware using massively parallel computations. For ordinary computers that are not massively parallel, optimization algorithms such as those in several SAS procedures are usually far more efficient. This talk shows how to fit neural networks using SAS/OR R fl , SAS/ETS R fl , and SAS/STAT R fl software.

Connected through hyperedge HE-1 (induced by source paper "Neural Networks and Statistical Models Proceedings of the Nineteenth Annual SAS Users Group International Conference,"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Introduction to Radial Basis Function Networks"
    - "What Size Neural Network Gives Optimal Generalization? Convergence Properties of Backpropagation"

Connected through hyperedge HE-2 (induced by source paper "Back Propagation is Sensitive to Initial Conditions"):
  with members:
    - "Multiassociative Memory"
    - "Experiments on the Transfer of Knowledge between Neural Networks Reprinted from: Computational Learning Theory and"
    - "Scaling-up RAAMs"
    - "Replicability of Neural Computing Experiments"
    - "Representing and Learning Visual Schemas in Neural Networks for Scene Analysis"
    - "Hyperplane Dynamics as a Means to Understanding Back-Propagation Learning and Network Plasticity"
    - "Statistical Tests for Comparing Supervised Classification Learning Algorithms"

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