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: "A Connectionist Architecture for Learning to Parse"
  Abstract: We present a connectionist architecture and demonstrate that it can learn syntactic parsing from a corpus of parsed text. The architecture can represent syntactic constituents, and can learn generalizations over syntactic constituents, thereby addressing the sparse data problems of previous connectionist architectures. We apply these Simple Synchrony Networks to mapping sequences of word tags to parse trees. After training on parsed samples of the Brown Corpus, the networks achieve precision and recall on constituents that approaches that of statistical methods for this task.

Connected through hyperedge HE-1 (induced by source paper "Simple Synchrony Networks Learning to Parse Natural Language with Temporal Synchrony Variable Binding"):
  with members:
    - "A Connectionist Architecture with Inherent Systematicity"

Connected through hyperedge HE-2 (induced by source paper "A Connectionist Architecture with Inherent Systematicity"):
  with members:
    - "Simple Synchrony Networks Learning to Parse Natural Language with Temporal Synchrony Variable Binding"

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