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: "On the Effect of Analog Noise in Discrete-Time Analog Computations"
  Abstract: We introduce a model for analog computation with discrete time in the presence of analog noise that is flexible enough to cover the most important concrete cases, such as noisy analog neural nets and networks of spiking neurons. This model subsumes the classical model for digital computation in the presence of noise. We show that the presence of arbitrarily small amounts of analog noise reduces the power of analog computational models to that of finite automata, and we also prove a new type of upper bound for the

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:
    - "Constructing Deterministic Finite-State Automata in Recurrent Neural Networks"

Connected through hyperedge HE-2 (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"
    - "Rule Revision with Recurrent Neural Networks"

Connected through hyperedge HE-3 (induced by source paper "TURING COMPUTABILITY WITH NEURAL NETS"):
  with members:
    (none)

Connected through hyperedge HE-4 (induced by source paper "Vapnik-Chervonenkis Dimension of Recurrent Neural Networks"):
  with members:
    - "Neural Networks with Quadratic VC Dimension"
    - "Sample Complexity for Learning Recurrent Perceptron Mappings"
    - "NEURAL NETS AS SYSTEMS MODELS AND CONTROLLERS suitability of "neural nets" as models for dynamical"
    - "POWER OF NEURAL NETS"
    - "What Size Neural Network Gives Optimal Generalization? Convergence Properties of Backpropagation"
    - "Networks of Spiking Neurons: The Third Generation of Neural Network Models"

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: Theory