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 "Using Many-Particle Decomposition to get a Parallel Self-Organising Map"):
  Abstract: We propose a method for decreasing the computational complexity of self-organising maps. The method uses a partitioning of the neurons into disjoint clusters. Teaching of the neurons occurs on a cluster-basis instead of on a neuron-basis. For teaching an N-neuron network with N 0 samples, the computational complexity decreases from O(N 0 N) to O(N 0 log N). Furthermore, we introduce a measure for the amount of order in a self-organising map, and show that the introduced algorithm behaves as well as the original algorithm.

Includes members:
  Member "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Soft Computing: the Convergence of Emerging Reasoning Technologies")
  Member "Approximation with neural networks: Between local and global approximation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Constructive Training Methods for Feedforward Neural Networks with Binary Weights")

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