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: "REDUCED MEMORY REPRESENTATIONS FOR MUSIC"
  Abstract: We address the problem of musical variation (identification of different musical sequences as variations) and its implications for mental representations of music. According to reductionist theories, listeners judge the structural importance of musical events while forming mental representations. These judgments may result from the production of reduced memory representations that retain only the musical gist. In a study of improvised music performance, pianists produced variations on melodies. Analyses of the musical events retained across variations provided support for the reductionist account of structural importance. A neural network trained to produce reduced memory representations for the same melodies represented structurally important events more efficiently than others. Agreement among the musicians' improvisations, the network model, and music-theoretic predictions suggest that perceived constancy across musical variation is a natural result of a reductionist mechanism for producing memory representations.

Connected through hyperedge HE-1 (induced by source paper "Induction of Multiscale Temporal Structure"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Tau Net: A Neural Network for Modeling Temporal Variability"
    - "Neural net architectures for temporal sequence processing"
    - "Learning Sequential Tasks by Incrementally Adding Higher Orders"
    - "A Connectionist Symbol Manipulator That Discovers the Structure of Context-Free Languages"
    - "Diffusion of Context and Credit Information in Markovian Models"
    - "Sequence Learning with Incremental Higher-Order Neural Networks"

Connected through hyperedge HE-2 (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"
    - "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