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: "Induction of Multiscale Temporal Structure"
  Abstract: Learning structure in temporally-extended sequences is a difficult computational problem because only a fraction of the relevant information is available at any instant. Although variants of back propagation can in principle be used to find structure in sequences, in practice they are not sufficiently powerful to discover arbitrary contingencies, especially those spanning long temporal intervals or involving high order statistics. For example, in designing a connectionist network for music composition, we have encountered the problem that the net is able to learn musical structure that occurs locally in time|e.g., relations among notes within a musical phrase|but not structure that occurs over longer time periods|e.g., relations among phrases. To address this problem, we require a means of constructing a reduced description of the sequence that makes global aspects more explicit or more readily detectable. I propose to achieve this using hidden units that operate with different time constants. Simulation experiments indicate that slower time-scale hidden units are able to pick up global structure, structure that simply can not be learned by standard Many patterns in the world are intrinsically temporal, e.g., speech, music, the unfolding of events. Recurrent neural net architectures have been devised to accommodate time-varying sequences. For example, the architecture shown in Figure 1 can map a sequence of inputs to a sequence of outputs. Learning structure in temporally-extended sequences is a difficult computational problem because the input pattern may not contain all the task-relevant information at any instant. Thus, back propagation.

Connected through hyperedge HE-1 (induced by source paper "REDUCED MEMORY REPRESENTATIONS FOR MUSIC"):
  with members:
    - "RESONANCE AND THE PERCEPTION OF MUSICAL METER"

Connected through hyperedge HE-2 (induced by source paper "Diffusion of Context and Credit Information in Markovian Models"):
  with members:
    - "Hierarchical Recurrent Neural Networks for Long-Term Dependencies"

Connected through hyperedge HE-3 (induced by source paper "Learning Sequential Tasks by Incrementally Adding Higher Orders"):
  with members:
    - "A Connectionist Symbol Manipulator That Discovers the Structure of Context-Free Languages"
    - "Sequence Learning with Incremental Higher-Order Neural Networks"
    - "The Functional Transfer of Knowledge for Coronary Artery Disease Diagnosis"

Connected through hyperedge HE-4 (induced by source paper "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"):
  with members:
    - "Representing and Learning Visual Schemas in Neural Networks for Scene Analysis"
    - "Convergence-Zone Episodic Memory: Analysis and Simulations"
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Dyslexic and Category-Specific Aphasic Impairments in a Self-Organizing Feature Map Model of the Lexicon"
    - "Neural Networks and Statistical Models Proceedings of the Nineteenth Annual SAS Users Group International Conference,"
    - "Boltzmann Machine learning using mean field theory and linear response correction"
    - "Misclassification Minimization"

Connected through hyperedge HE-5 (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"
    - "A FIXED SIZE STORAGE O(n 3 TIME COMPLEXITY LEARNING ALGORITHM FOR FULLY RECURRENT CONTINUALLY RUNNING"
    - "RESONANCE AND THE PERCEPTION OF MUSICAL METER"

Connected through hyperedge HE-6 (induced by source paper "A Connectionist Symbol Manipulator That Discovers the Structure of Context-Free Languages"):
  with members:
    - "Learning Sequential Tasks by Incrementally Adding Higher Orders"
    - "Learning Context-free Grammars: Capabilities and Limitations of a Recurrent Neural Network with an External Stack Memory"

Connected through hyperedge HE-7 (induced by source paper "Sequence Learning with Incremental Higher-Order Neural Networks"):
  with members:
    - "Learning Sequential Tasks by Incrementally Adding Higher Orders"

Connected through hyperedge HE-8 (induced by source paper "Tau Net: A Neural Network for Modeling Temporal Variability"):
  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