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: "Extraction of Rules from Discrete-Time Recurrent Neural Networks"
  Abstract: The extraction of symbolic knowledge from trained neural networks and the direct encoding of (partial) knowledge into networks prior to training are important issues. They allow the exchange of information between symbolic and connectionist knowledge representations. The focus of this paper is on the quality of the rules that are extracted from recurrent neural networks. Discrete-time recurrent neural networks can be trained to correctly classify strings of a regular language. Rules defining the learned grammar can be extracted from networks in the form of deterministic finite-state automata (DFA's) by applying clustering algorithms in the output space of recurrent state neurons. Our algorithm can extract different finite-state automata that are consistent with a training set from the same network. We compare the generalization performances of these different models and the trained network and we introduce a heuristic that permits us to choose among the consistent DFA's the model which best approximates the learned regular grammar. Keywords: Recurrent Neural Networks, Grammatical Inference, Regular Languages, Deterministic Finite-State Automata, Rule Extraction, Generalization Performance, Model Selection, Occam's Razor. fl Technical Report CS-TR-3465 and UMIACS-TR-95-54, University of Maryland, College Park, MD 20742. Ac cepted for publication in Neural Networks.

Connected through hyperedge HE-1 (induced by source paper "Rule Revision with Recurrent Neural Networks"):
  with members:
    - "Constructing Deterministic Finite-State Automata in Recurrent Neural Networks"
    - "Learning Context-free Grammars: Capabilities and Limitations of a Recurrent Neural Network with an External Stack Memory"
    - "A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction"

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

Connected through hyperedge HE-3 (induced by source paper "Noisy Time Series Prediction using Symbolic Representation and Recurrent Neural Network Grammatical Inference"):
  with members:
    - "PREDICTING SUNSPOTS AND EXCHANGE RATES WITH CONNECTIONIST NETWORKS"
    - "Draft Symbolic Representation of Neural Networks"
    - "POWER OF NEURAL NETS"
    - "Statistical Mechanics of Nonlinear Nonequilibrium Financial Markets: Applications to Optimized Trading"

Connected through hyperedge HE-4 (induced by source paper "Pruning Recurrent Neural Networks for Improved Generalization Performance"):
  with members:
    - "A Delay Damage Model Selection Algorithm for NARX Neural Networks"
    - "Using Recurrent Neural Networks to Learn the Structure of Interconnection Networks"
    - "Evolving Artificial Neural Networks using the Baldwin Effect"
    - "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize 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