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: "RESONANCE AND THE PERCEPTION OF MUSICAL METER"
  Abstract: Many connectionist approaches to musical expectancy and music composition let the question of What next? overshadow the equally important question of When next?. One cannot escape the latter question, one of temporal structure, when considering the perception of musical meter. We view the perception of metrical structure as a dynamic process where the temporal organization of external musical events synchronizes, or entrains, a listeners internal processing mechanisms. This article introduces a novel connectionist unit, based upon a mathematical model of entrainment, capable of phase and frequency-locking to periodic components of incoming rhythmic patterns. Networks of these units can self-organize temporally structured responses to rhythmic patterns. The resulting network behavior embodies the perception of metrical structure. The article concludes with a discussion of the implications of our approach for theories of metrical structure and musical expectancy.

Connected through hyperedge HE-1 (induced by source paper "REDUCED MEMORY REPRESENTATIONS FOR MUSIC"):
  with members:
    - "Induction of Multiscale Temporal Structure"

Connected through hyperedge HE-2 (induced by source paper "Representing Rhythmic Patterns in a Network of Oscillators"):
  with members:
    - "Meter as Mechanism: A Neural Network that Learns Metrical Patterns"

Connected through hyperedge HE-3 (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"
    - "Representing Rhythmic Patterns in a Network of Oscillators"

Connected through hyperedge HE-4 (induced by source paper "Neural net architectures for temporal sequence processing"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "PREDICTING SUNSPOTS AND EXCHANGE RATES WITH CONNECTIONIST NETWORKS"
    - "Induction of Multiscale Temporal Structure"
    - "A FIXED SIZE STORAGE O(n 3 TIME COMPLEXITY LEARNING ALGORITHM FOR FULLY RECURRENT CONTINUALLY RUNNING"

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