human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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

Includes members:
  Member "Natural Gradient Descent for Training Multi-Layer Perceptrons":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Equivariant adaptive source separation")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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