human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.)

Includes members:
  Member "Constructing Deterministic Finite-State Automata in Recurrent Neural Networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Analog Neural Nets with Gaussian or other Common Noise Distributions cannot Recognize Arbitrary Regular Languages")
  Member "Extraction of Rules from Discrete-Time Recurrent Neural Networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Analysis of Dynamical Recognizers")
  Member "Learning Context-free Grammars: Capabilities and Limitations of a Recurrent Neural Network with an External Stack Memory":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Using Prior Knowledge in an NNPDA to Learn Context-Free Languages")
  Member "A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Using Recurrent Neural Networks to Learn the Structure of Interconnection Networks")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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