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 Unifying View of Some Training Algorithms for Multilayer Perceptrons with FIR Filter Synapses"
  Abstract: Recent interest has come about in deriving various neural network architectures for modelling time-dependent signals. A number of algorithms have been published for multilayer perceptrons with synapses described by finite impulse response (FIR) and infinite impulse response (IIR) filters (the latter case is also known as Locally Recurrent Globally Feedforward Networks). The derivations of these algorithms have used different approaches in calculating the gradients, and in this note, we present a short, but unifying account of how these different algorithms compare for the FIR case, both in derivation, and performance. New algorithms are subsequently presented. Simulation results have been performed to benchmark these algorithms. In this note, results are compared for the Mackey-Glass chaotic time series against a number of other methods including a standard multilayer perceptron, and a local approximation method.

Connected through hyperedge HE-1 (induced by source paper "On the Distribution of Performance from Multiple Neural Network Trials, On the Distribution of Performance"):
  with members:
    - "What Size Neural Network Gives Optimal Generalization? Convergence Properties of Backpropagation"
    - "Lessons in Neural Network Training: Overfitting Lessons in Neural Network Training: Overfitting May be Harder"
    - "Exponentially many local minima for single neurons"
    - "Statistical Evaluation of Neural Network Experiments: Minimum Requirements and Current Practice"

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