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 "Local Feedforward Networks"):
  Abstract: Although feedforward neural networks are well suited to function approximation, in some applications networks experience problems when learning a desired function. One problem is interference which 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 that incorporates 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-prop learning algorithm, we address a familiar misconception that sigmoidal networks are inherently non-local by demonstrating that given a sufficiently large number of adjustable parameters, sigmoidal MLPs can be made arbitrarily local while retaining the ability to represent any continuous function on a compact domain.

Includes members:
  Member "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Induction of Multiscale Temporal Structure")
  Member "An Analytical Framework for Local Feedforward Networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Adaptive Wavelet Control of Nonlinear Systems")

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