human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "A Connectionist Architecture with Inherent Systematicity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Simple Synchrony Networks Learning to Parse Natural Language with Temporal Synchrony Variable Binding")
  Member "Simple Synchrony Networks Learning to Parse Natural Language with Temporal Synchrony Variable Binding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "A Connectionist Architecture with Inherent Systematicity")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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