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: "Simple Synchrony Networks Learning to Parse Natural Language with Temporal Synchrony Variable Binding"
  Abstract: The Simple Synchrony Network (SSN) is a new connectionist architecture, incorporating the insights of Temporal Synchrony Variable Binding (TSVB) into Simple Recurrent Networks. The use of TSVB means SSNs can output representations of structures, and can learn generalisations over the constituents of these structures (as required by systematicity). This paper describes the SSN and an associated training algorithm, and demonstrates SSNs' generalisation abilities through results from training SSNs to parse real natural language sentences.

Connected through hyperedge HE-1 (induced by source paper "A Connectionist Architecture with Inherent Systematicity"):
  with members:
    - "A Connectionist Architecture for Learning to Parse"

Connected through hyperedge HE-2 (induced by source paper "A Connectionist Architecture for Learning to Parse"):
  with members:
    - "A Connectionist Architecture with Inherent Systematicity"

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