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: "Vapnik-Chervonenkis Dimension of Recurrent Neural Networks"
  Abstract: Most of the work on the Vapnik-Chervonenkis dimension of neural networks has been focused on feedforward networks. However, recurrent networks are also widely used in learning applications, in particular when time is a relevant parameter. This paper provides lower and upper bounds for the VC dimension of such networks. Several types of activation functions are discussed, including threshold, polynomial, piecewise-polynomial and sigmoidal functions. The bounds depend on two independent parameters: the number w of weights in the network, and the length k of the input sequence. In contrast, for feedforward networks, VC dimension bounds can be expressed as a function of w only. An important difference between recurrent and feedforward nets is that a fixed recurrent net can receive inputs of arbitrary length. Therefore we are particularly interested in the case k w. Ignoring multiplicative constants, the main results say roughly the following: * For architectures with activation = any fixed nonlinear polynomial, the VC dimension is wk. * For architectures with activation = any fixed piecewise polynomial, the VC dimension is between wk and w 2 k. * For architectures with activation = H (threshold nets), the VC dimension is between w log(k=w) and minfwk log wk; w 2 + w log wkg. * For the standard sigmoid (x) = 1=(1 + e x ), the VC dimension is between wk and w 4 k 2 . An earlier version of this paper has appeared in Proc. 3rd European Workshop on Computational Learning Theory, LNCS 1208, pages 223-237, Springer, 1997.

Connected through hyperedge HE-1 (induced by source paper "POWER OF NEURAL NETS"):
  with members:
    - "Dimension of Recurrent Neural Networks"
    - "Facing The Facts: Necessary Requirements For The Artificial Evolution of Complex Behaviour"
    - "Fault-Tolerant Implementation of Finite-State Automata in Recurrent Neural Networks"
    - "Noisy Time Series Prediction using Symbolic Representation and Recurrent Neural Network Grammatical Inference"
    - "Can Recurrent Neural Networks Learn Natural Language Grammars? W&Z recurrent neural networks are able to"
    - "Interconnected Automata and Linear Systems: A Theoretical Framework in Discrete-Time In Hybrid Systems III: Verification"
    - "First-Order vs. Second-Order Single Layer Recurrent Neural Networks"

Connected through hyperedge HE-2 (induced by source paper "Sample Complexity for Learning Recurrent Perceptron Mappings"):
  with members:
    - "Dimension of Recurrent Neural Networks"
    - "LINEAR SYSTEMS WITH SIGN-OBSERVATIONS"

Connected through hyperedge HE-3 (induced by source paper "NEURAL NETS AS SYSTEMS MODELS AND CONTROLLERS suitability of "neural nets" as models for dynamical"):
  with members:
    - "Dimension of Recurrent Neural Networks"
    - "NETWORKS, FUNCTION DETERMINES FORM"
    - "Identification and Control of Nonlinear Systems Using Neural Network Models: Design and Stability Analysis"
    - "FEEDBACK STABILIZATION USING TWO-HIDDEN-LAYER NETS"
    - "Recurrent Neural Networks: Some Systems-Theoretic Aspects"

Connected through hyperedge HE-4 (induced by source paper "Networks of Spiking Neurons: The Third Generation of Neural Network Models"):
  with members:
    - "Shattering all sets of k points in "general position" requires (k 1)=2 parameters"
    - "An Efficient Implementation of Sigmoidal Neural Nets in Temporal Coding with Noisy Spiking Neurons"
    - "A Delay-Line Based Motion Detection Chip"

Connected through hyperedge HE-5 (induced by source paper "Neural Networks with Quadratic VC Dimension"):
  with members:
    - "Dimension of Recurrent Neural Networks"
    - "Shattering all sets of k points in "general position" requires (k 1)=2 parameters"
    - "Similar Classifiers and VC Error Bounds"

Connected through hyperedge HE-6 (induced by source paper "What Size Neural Network Gives Optimal Generalization? Convergence Properties of Backpropagation"):
  with members:
    - "Neural Networks and Statistical Models Proceedings of the Nineteenth Annual SAS Users Group International Conference,"
    - "Statistical Ideas for Selecting Network Architectures"
    - "Lessons in Neural Network Training: Overfitting Lessons in Neural Network Training: Overfitting May be Harder"
    - "On the Distribution of Performance from Multiple Neural Network Trials, On the Distribution of Performance"

Connected through hyperedge HE-7 (induced by source paper "On the Effect of Analog Noise in Discrete-Time Analog Computations"):
  with members:
    - "Constructing Deterministic Finite-State Automata in 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