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: "Constructing Deterministic Finite-State Automata in Recurrent Neural Networks"
  Abstract: Recurrent neural networks that are trained to behave like deterministic finite-state automata (DFAs) can show deteriorating performance when tested on long strings. This deteriorating performance can be attributed to the instability of the internal representation of the learned DFA states. The use of a sigmoidal discriminant function together with the recurrent structure contribute to this instability. We prove that a simple algorithm can construct second-order recurrent neural networks with a sparse interconnection topology and sigmoidal discriminant function such that the internal DFA state representations are stable, i.e. the constructed network correctly classifies strings of arbitrary length. The algorithm is based on encoding strengths of weights directly into the neural network. We derive a relationship between the weight strength and the number of DFA states for robust string classification. For a DFA with n states and m input alphabet symbols, the constructive algorithm generates a "programmed" neural network with O(n) neurons and O(mn) weights. We compare our algorithm to other methods proposed in the literature.

Connected through hyperedge HE-1 (induced by source paper "Analog Neural Nets with Gaussian or other Common Noise Distributions cannot Recognize Arbitrary Regular Languages"):
  with members:
    - "On the Effect of Analog Noise in Discrete-Time Analog Computations"

Connected through hyperedge HE-2 (induced by source paper "Rule Revision with Recurrent Neural Networks"):
  with members:
    - "Extraction of Rules from Discrete-Time 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-3 (induced by source paper "Fault-Tolerant Implementation of Finite-State Automata in Recurrent Neural Networks"):
  with members:
    - "POWER OF NEURAL NETS"

Connected through hyperedge HE-4 (induced by source paper "A Brief History of Connectionism"):
  with members:
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
    - "Learning networks for face analysis and synthesis"
    - "Bayesian Unsupervised Learning of Higher Order Structure"
    - "The end of the line for a brain-damaged model of unilateral neglect"

Connected through hyperedge HE-5 (induced by source paper "On the Effect of Analog Noise in Discrete-Time Analog Computations"):
  with members:
    - "Vapnik-Chervonenkis Dimension of Recurrent Neural Networks"
    - "Analog Neural Nets with Gaussian or other Common Noise Distributions cannot Recognize Arbitrary Regular Languages"
    - "TURING COMPUTABILITY WITH NEURAL NETS"

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