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: "An Analytical Framework for Local Feedforward Networks"
  Abstract: Interference in neural networks occurs when learning in one area of the input space causes unlearning in another area. Networks that are less susceptible to interference are referred to as spatially local networks. To understand these properties, a theoretical framework, consisting of a measure of interference and a measure of network localization, is developed. These measures incorporate not only the network weights and architecture but also the learning algorithm. Using this framework to analyze sigmoidal, multi-layer perceptron (MLP) networks that employ the back-propagation learning algorithm, we address a familiar misconception that single-hidden-layer sigmoidal networks are inherently non-local by demonstrating that given a sufficiently large number of adjustable weights, single-hidden-layer sigmoidal MLPs can be made arbitrarily local while retaining the ability to approximate any continuous function on a compact domain. fl Partially supported under Task 2312 R1 by the United States Air Force Office of Scientific Research.

Connected through hyperedge HE-1 (induced by source paper "Adaptive Wavelet Control of Nonlinear Systems"):
  with members:
    - "Space-Frequency Localized Basis Function Networks for Nonlinear System Estimation and Control"

Connected through hyperedge HE-2 (induced by source paper "Local Feedforward Networks"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"

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