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: "Fools Gold: Extracting Finite State Machines From Recurrent Network Dynamics"
  Abstract: Several recurrent networks have been proposed as representations for the task of formal language learning. After training a recurrent network, the next step is to understand the information processing carried out by the network. Some researchers (Giles et al., 1992; Watrous & Kuhn, 1992; Cleeremans et al., 1989) have resorted to extracting finite state machines from the internal state trajectories of their recurrent networks. This paper describes two conditions, sensitivity to initial conditions and frivolous computational explanations due to discrete measurements (Kolen & Pollack, 1993), which allow these extraction methods to return illusionary finite state descriptions.

Connected through hyperedge HE-1 (induced by source paper "Analysis of Dynamical Recognizers"):
  with members:
    - "Extraction of Rules from Discrete-Time 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