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: "Meter as Mechanism: A Neural Network that Learns Metrical Patterns"
  Abstract: One kind of prosodic structure that apparently underlies both music and language is meter. Yet detailed measurements of both music and speech show that the nested periodicities that define metrical structure are noisy in some sense. What kind of system could produce or perceive such variable metrical timing? And what would it take to store particular metrical patterns in the long-term memory of the system? We have developed a network of coupled oscillators that both produces and perceives metrical patterns of pulses. In addition, beginning with an initial state with no biases, it learns to prefer 3-beat patterns (like waltzes) over 2-beat patterns. Models of this general class could learn to entrain to musical patterns. And given a way to process speech to extract appropriate pulses, the model should be applicable to metrical structure in speech as well. Is language metrical? Meter refers both to particular sorts of patterns in time and to an abstract description of such patterns, potentially a cognitive representation of them. In both cases there are two or more hierarchical levels at which equally spaced events occur, and the periods characterizing these levels are integral multiples of each other (usually 2 or 3). The hierarchy is implied in standard Western musical notation, where the different levels are indicated by kinds of notes (quarter notes, half notes, etc.) and by bars separating measures. For example, in a basic waltz-time meter, there are individual beats, all with the same spacing, grouped into sets of three with every third one receiving a stronger accent. In such a meter, there is a hierarchy consisting of both a faster periodic cycle (at the beat level) and a slower one (the measure level) that is 1/3 as fast with its onset (or zero phase angle) coinciding with the zero phase angle of every third beat. Metrical systems like this seem to underlie most forms of music around the world and are often said to underlie human speech as well (Jones, 1932; Martin, 1972). However, an awkward difficulty is that the definition employs the notion of an integer since data on both music and speech show clearly that the perfect temporal ratios predicted by such a definition are not observed in performance. In music performance, various kinds of systematic temporal deviations in the timing specified by musical notation are known to

Connected through hyperedge HE-1 (induced by source paper "RESONANCE AND THE PERCEPTION OF MUSICAL METER"):
  with members:
    - "Neural net architectures for temporal sequence processing"
    - "REDUCED MEMORY REPRESENTATIONS FOR MUSIC"
    - "Representing Rhythmic Patterns in a Network of Oscillators"

Connected through hyperedge HE-2 (induced by source paper "Representing Rhythmic Patterns in a Network of Oscillators"):
  with members:
    - "RESONANCE AND THE PERCEPTION OF MUSICAL METER"

Connected through hyperedge HE-3 (induced by source paper "Synchronization and Desynchronization in a Network of Locally Coupled Wilson-Cowan Oscillators"):
  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