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: "Can Recurrent Neural Networks Learn Natural Language Grammars? W&Z recurrent neural networks are able to"
  Abstract: Recurrent neural networks are complex parametric dynamic systems that can exhibit a wide range of different behavior. We consider the task of grammatical inference with recurrent neural networks. Specifically, we consider the task of classifying natural language sentences as grammatical or ungrammatical can a recurrent neural network be made to exhibit the same kind of discriminatory power which is provided by the Principles and Parameters linguistic framework, or Government and Binding theory? We attempt to train a network, without the bifurcation into learned vs. innate components assumed by Chomsky, to produce the same judgments as native speakers on sharply grammatical/ungrammatical data. We consider how a recurrent neural network could possess linguistic capability, and investigate the properties of Elman, Narendra & Parthasarathy (N&P) and Williams & Zipser (W&Z) recurrent networks, and Frasconi-Gori-Soda (FGS) locally recurrent networks in this setting. We show that both

Connected through hyperedge HE-1 (induced by source paper "On the Applicability of Neural Network and Machine Learning Methodologies to Natural Language Processing"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Learning Feature-based Semantics with Simple Recurrent Networks"

Connected through hyperedge HE-2 (induced by source paper "POWER OF NEURAL NETS"):
  with members:
    - "Vapnik-Chervonenkis Dimension of Recurrent Neural Networks"
    - "Dimension of Recurrent Neural Networks"
    - "Facing The Facts: Necessary Requirements For The Artificial Evolution of Complex Behaviour"
    - "Fault-Tolerant Implementation of Finite-State Automata in Recurrent Neural Networks"
    - "Noisy Time Series Prediction using Symbolic Representation and Recurrent Neural Network Grammatical Inference"
    - "Interconnected Automata and Linear Systems: A Theoretical Framework in Discrete-Time In Hybrid Systems III: Verification"
    - "First-Order vs. Second-Order Single Layer Recurrent Neural Networks"

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