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 "Fault-Tolerant Implementation of Finite-State Automata in Recurrent Neural Networks"):
  Abstract: Recently, we have proven that the dynamics of any deterministic finite-state automata (DFA) with n states and m input symbols can be implemented in a sparse second-order recurrent neural network (SORNN) with n + 1 state neurons and O(mn) second-order weights and sigmoidal discriminant functions [5]. We investigate how that constructive algorithm can be extended to fault-tolerant neural DFA implementations where faults in an analog implementation of neurons or weights do not affect the desired network performance. We show that tolerance to weight perturbation can be achieved easily; tolerance to weight and/or neuron stuck-at-zero faults, however, requires duplication of the network resources. This result has an impact on the construction of neural DFAs with a dense internal representation of DFA states.

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 "POWER OF NEURAL NETS":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Can Recurrent Neural Networks Learn Natural Language Grammars? W&Z recurrent neural networks are able to")

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