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: "Analysis of Dynamical Recognizers"
  Abstract: Pollack (1991) demonstrated that second-order recurrent neural networks can act as dynamical recognizers for formal languages when trained on positive and negative examples, and observed both phase transitions in learning and IFS-like fractal state sets. Follow-on work focused mainly on the extraction and minimization of a finite state automaton (FSA) from the trained network. However, such networks are capable of inducing languages which are not regular, and therefore not equivalent to any FSA. Indeed, it may be simpler for a small network to fit its training data by inducing such a non-regular language. But when is the network's language not regular? In this paper, using a low dimensional network capable of learning all the Tomita data sets, we present an empirical method for testing whether the language induced by the network is regular or not. We also provide a detailed "-machine analysis of trained networks for both regular and non-regular languages.

Connected through hyperedge HE-1 (induced by source paper "Extraction of Rules from Discrete-Time Recurrent Neural Networks"):
  with members:
    - "Pruning Recurrent Neural Networks for Improved Generalization Performance"
    - "Rule Revision with Recurrent Neural Networks"
    - "Noisy Time Series Prediction using Symbolic Representation and Recurrent Neural Network Grammatical Inference"

Connected through hyperedge HE-2 (induced by source paper "Fools Gold: Extracting Finite State Machines From Recurrent Network Dynamics"):
  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