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 "CLASSIFICATION USING HIERARCHICAL MIXTURES OF EXPERTS"):
  Abstract: There has recently been widespread interest in the use of multiple models for classification and regression in the statistics and neural networks communities. The Hierarchical Mixture of Experts (HME) [1] has been successful in a number of regression problems, yielding significantly faster training through the use of the Expectation Maximisation algorithm. In this paper we extend the HME to classification and results are reported for three common classification benchmark tests: Exclusive-Or, N-input Parity and Two Spirals.

Includes members:
  Member "Hierarchical Mixtures of Experts and the EM Algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Forecasting electricity demand using nonlinear mixture of experts")
  Member "On Convergence Properties of the EM Algorithm for Gaussian Mixtures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian")
  Member "Constructive Algorithms for Hierarchical Mixtures of Experts":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Hierarchical Mixtures of Experts and the EM Algorithm")

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