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 Multiple Node Types to Improve the Performance of DMP (Dynamic Multilayer Perceptron)"):
  Abstract: This paper discusses a method for training multilayer perceptron networks called DMP2 (Dynamic Multilayer Perceptron 2). The method is based upon a divide and conquer approach which builds networks in the form of binary trees, dynamically allocating nodes and layers as needed. The focus of this paper is on the effects of using multiple node types within the DMP framework. Simulation results show that DMP2 performs favorably in comparison with other learning algorithms, and that using multiple node types can be beneficial to network performance.

Includes members:
  Member "A Self-Adjusting Dynamic Logic Module":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Automatic Feature Extraction in Machine Learning")
  Member "A Provably Convergent Dynamic Training Method for Multilayer Perceptron Networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "A Self-Adjusting Dynamic Logic Module")

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