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: "Synchronization and Desynchronization in a Network of Locally Coupled Wilson-Cowan Oscillators"
  Abstract: A network of Wilson-Cowan oscillators is constructed, and its emergent properties of synchronization and desynchronization are investigated by both computer simulation and formal analysis. The network is a two-dimensional matrix, where each oscillator is coupled only to its neighbors. We show analytically that a chain of locally coupled oscillators (the piece-wise linear approximation to the Wilson-Cowan oscillator) synchronizes, and present a technique to rapidly entrain finite numbers of oscillators. The coupling strengths change on a fast time scale based on a Hebbian rule. A global separator is introduced which receives input from and sends feedback to each oscillator in the matrix. The global separator is used to desynchronize different oscillator groups. Unlike many other models, the properties of this network emerge from local connections, that preserve spatial relationships among components, and are critical for encoding Gestalt principles of feature grouping. The ability to synchronize and desynchronize oscillator groups within this network offers a promising approach for pattern segmentation and figure/ground segregation based on oscillatory correlation.

Connected through hyperedge HE-1 (induced by source paper "Meter as Mechanism: A Neural Network that Learns Metrical Patterns"):
  with members:
    - "RESONANCE AND THE PERCEPTION OF MUSICAL METER"
    - "Representing Rhythmic Patterns in a Network of Oscillators"

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