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: "Object Selection Based on Oscillatory Correlation"
  Abstract: 1 Technical Report: OSU-CISRC-12/96 - TR67, 1996 Abstract One of the classical topics in neural networks is winner-take-all (WTA), which has been widely used in unsupervised (competitive) learning, cortical processing, and attentional control. Because of global connectivity, WTA networks, however, do not encode spatial relations in the input, and thus cannot support sensory and perceptual processing where spatial relations are important. We propose a new architecture that maintains spatial relations between input features. This selection network builds on LEGION (Locally Excitatory Globally Inhibitory Oscillator Networks) dynamics and slow inhibition. In an input scene with many objects (patterns), the network selects the largest object. This system can be easily adjusted to select several largest objects, which then alternate in time. We further show that a twostage selection network gains efficiency by combining selection with parallel removal of noisy regions. The network is applied to select the most salient object in real images. As a special case, the selection network without local excitation gives rise to a new form of oscillatory WTA.

Connected through hyperedge HE-1 (induced by source paper "Fast Numerical Integration of Relaxation Oscillator Networks Based on Singular Limit Solutions"):
  with members:
    (none)

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