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: "Analog Neural Nets with Gaussian or other Common Noise Distributions cannot Recognize Arbitrary Regular Languages"
  Abstract: We consider recurrent analog neural nets where the output of each gate is subject to Gaussian noise, or any other common noise distribution that is nonzero on a large set. We show that many regular languages cannot be recognized by networks of this type, and we give a precise characterization of those languages which can be recognized. This result implies severe constraints on possibilities for constructing recurrent analog neural nets that are robust against realistic types of analog noise. On the other hand we present a method for constructing feedforward analog neural nets that are robust with regard to analog noise of this type.

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"
    - "A Brief History of Connectionism"
    - "Rule Revision with Recurrent Neural Networks"
    - "On the Effect of Analog Noise in Discrete-Time Analog Computations"

Connected through hyperedge HE-2 (induced by source paper "On the Effect of Analog Noise in Discrete-Time Analog Computations"):
  with members:
    - "Vapnik-Chervonenkis Dimension of Recurrent Neural Networks"
    - "Constructing Deterministic Finite-State Automata in Recurrent Neural Networks"
    - "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