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: "Rule Revision with Recurrent Neural Networks"
  Abstract: Recurrent neural networks readily process, recognize and generate temporal sequences. By encoding grammatical strings as temporal sequences, recurrent neural networks can be trained to behave like deterministic sequential finite-state automata. Algorithms have been developed for extracting grammatical rules from trained networks. Using a simple method for inserting prior knowledge (or rules) into recurrent neural networks, we show that recurrent neural networks are able to perform rule revision. Rule revision is performed by comparing the inserted rules with the rules in the finite-state automata extracted from trained networks. The results from training a recurrent neural network to recognize a known non-trivial, randomly generated regular grammar show that not only do the networks preserve correct rules but that they are able to correct through training inserted rules which were initially incorrect. (By incorrect, we mean that the rules were not the ones in the randomly generated grammar.)

Connected through hyperedge HE-1 (induced by source paper "Constructing Deterministic Finite-State Automata in Recurrent Neural Networks"):
  with members:
    - "Fault-Tolerant Implementation of Finite-State Automata in Recurrent Neural Networks"
    - "Analog Neural Nets with Gaussian or other Common Noise Distributions cannot Recognize Arbitrary Regular Languages"
    - "A Brief History of Connectionism"
    - "On the Effect of Analog Noise in Discrete-Time Analog Computations"

Connected through hyperedge HE-2 (induced by source paper "Extraction of Rules from Discrete-Time Recurrent Neural Networks"):
  with members:
    - "Pruning Recurrent Neural Networks for Improved Generalization Performance"
    - "Noisy Time Series Prediction using Symbolic Representation and Recurrent Neural Network Grammatical Inference"
    - "Analysis of Dynamical Recognizers"

Connected through hyperedge HE-3 (induced by source paper "Learning Context-free Grammars: Capabilities and Limitations of a Recurrent Neural Network with an External Stack Memory"):
  with members:
    - "A Connectionist Symbol Manipulator That Discovers the Structure of Context-Free Languages"
    - "Using Prior Knowledge in an NNPDA to Learn Context-Free Languages"
    - "AN ADAPTIVE NEURAL NETWORK PARSER"

Connected through hyperedge HE-4 (induced by source paper "A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction"):
  with members:
    - "A Stochastic Search Approach to Grammar Induction"
    - "Using Recurrent Neural Networks to Learn the Structure of Interconnection 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