human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent groups of papers sharing a common citing paper.

Details of the hypergraph:
Center node: "A Provably Convergent Dynamic Training Method for Multilayer Perceptron Networks"
  Abstract: This paper presents a new method for training multilayer perceptron networks called DMP1 (Dynamic Multilayer Perceptron 1). 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 individual nodes of the network are trained using a gentetic algorithm. The method is capable of handling real-valued inputs and a proof is given concerning its convergence properties of the basic model. Simulation results show that DMP1 performs favorably in comparison with other learning algorithms.

Connected through hyperedge HE-1 (induced by source paper "Using Multiple Node Types to Improve the Performance of DMP (Dynamic Multilayer Perceptron)"):
  with members:
    - "A Self-Adjusting Dynamic Logic Module"

Connected through hyperedge HE-2 (induced by source paper "A Self-Adjusting Dynamic Logic Module"):
  with members:
    - "A Self-Organizing Binary Decision Tree For Incrementally Defined Rule Based"
    - "Automatic Feature Extraction in Machine Learning"
    - "Using Multiple Node Types to Improve the Performance of DMP (Dynamic Multilayer Perceptron)"
    - "The Effect of Decision Surface Fitness on Dynamic Multilayer Perceptron Networks (DMP1)"
    - "Towards a General Distributed Platform for Learning and Generalization and Word Perfect Corp. 1 Introduction"
    - "Word Perfect Corp. LIA: A Location-Independent Transformation for ASOCS Adaptive Algorithm 2"
    - "Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING EFFICIENT DYNAMIC BACKPROPAGATION NEURAL NETWORKS"
    - "A VLSI Implementation of a Parallel, Self-Organizing Learning Model"

Question: Please tell me which class the center node 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