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: "NIPS*97 The Efficiency and The Robustness of Natural Gradient Descent Learning Rule"
  Abstract: We have discovered a new scheme to represent the Fisher information matrix of a stochastic multi-layer perceptron. Based on this scheme, we have designed an algorithm to compute the inverse of the Fisher information matrix. When the input dimension n is much larger than the number of hidden neurons, the complexity of this algorithm is of order O(n 2 ) while the complexity of conventional algorithms for the same purpose is of order O(n 3 ). The inverse of the Fisher information matrix is used in the natural gradient descent algorithm to train single-layer or multi-layer perceptrons. It is confirmed by simulation that the natural gradient

Connected through hyperedge HE-1 (induced by source paper "Natural Gradient Descent for Training Multi-Layer Perceptrons"):
  with members:
    - "Equivariant adaptive source separation"
    - "Statistical Theory of Overtraining Is Cross-Validation Asymptotically Effective?"

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