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: "Representing Rhythmic Patterns in a Network of Oscillators"
  Abstract: This paper describes an evolving computational model of the perception and production of simple rhythmic patterns. The model consists of a network of oscillators of different resting frequencies which couple with input patterns and with each other. Oscillators whose frequencies match periodicities in the input tend to become activated. Metrical structure is represented explicitly in the network in the form of clusters of oscillators whose frequencies and phase angles are constrained to maintain the harmonic relationships that characterize meter. Rests in rhythmic patterns are represented by explicit rest oscillators in the network, which become activated when an expected beat in the pattern fails to appear. The model makes predictions about the relative difficulty of The nested periodicity that defines musical, and probably also linguistic, meter appears to be fundamental to the way in which people perceive and produce patterns in time. Meter by itself, however, is not sufficient to describe patterns which are interesting or memorable because of how they deviate from the metrical hierarchy. The simplest deviations are rests or gaps where one or more levels in the hierarchy would normally have a beat. When beats are removed at regular intervals which match the period of some level of the metrical hierarchy, we have what we will call a simple rhythmic pattern. Figure 1 shows an example of a simple rhythmic pattern. Below it is a grid representation of the meter which is behind the pattern. patterns and the effect of deviations from periodicity in the input.

Connected through hyperedge HE-1 (induced by source paper "RESONANCE AND THE PERCEPTION OF MUSICAL METER"):
  with members:
    - "Meter as Mechanism: A Neural Network that Learns Metrical Patterns"
    - "Neural net architectures for temporal sequence processing"
    - "REDUCED MEMORY REPRESENTATIONS FOR MUSIC"

Connected through hyperedge HE-2 (induced by source paper "Meter as Mechanism: A Neural Network that Learns Metrical Patterns"):
  with members:
    - "Synchronization and Desynchronization in a Network of Locally Coupled Wilson-Cowan Oscillators"
    - "RESONANCE AND THE PERCEPTION OF MUSICAL METER"

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