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 "Natural Gradient Descent for Training Multi-Layer Perceptrons"):
  Abstract: The main difficulty in implementing the natural gradient learning rule is to compute the inverse of the Fisher information matrix when the input dimension is large. We have found a new scheme to represent the Fisher information matrix. 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 simulation has confirmed the efficience and robustness of the natural gradient learning rule.

Includes members:
  Member "Equivariant adaptive source separation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "A New Learning Algorithm for Blind Signal Separation")
  Member "Statistical Theory of Overtraining Is Cross-Validation Asymptotically Effective?":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Early Stopping but when?")
  Member "NIPS*97 The Efficiency and The Robustness of Natural Gradient Descent Learning Rule":
    also connected through other hyperedges:
      (none)

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