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 Generalisations across Syntactic Constituents"
  Abstract: This paper describes a training algorithm for Simple Synchrony Networks (SSNs), and reports on experiments in language learning using a recursive grammar. The SSN is a new connectionist architecture combining a technique for learning about patterns across time, Simple Recurrent Networks (SRNs), with Temporal Synchrony Variable Binding (TSVB). The use of TSVB means the SSN can learn about entities in the training set, and generalise this information to entities in the test set. In the experiments, the network is trained on sentences with up to one embedded clause, and with some words restricted to certain classes of constituent. During testing, the network generalises information learned to sentences with up to three embedded clauses, and with words appearing in any constituent. These results demonstrate that SSNs learn generalisations across syntactic constituents.

Connected through hyperedge HE-1 (induced by source paper "Natural Language Grammatical Inference with Recurrent Neural Networks"):
  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