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: "Dimension of Recurrent Neural Networks"
  Abstract: DIMACS Technical Report 96-56 December 1996

Connected through hyperedge HE-1 (induced by source paper "POWER OF NEURAL NETS"):
  with members:
    - "Vapnik-Chervonenkis 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:
    - "Vapnik-Chervonenkis 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:
    - "Vapnik-Chervonenkis 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 "Neural Networks with Quadratic VC Dimension"):
  with members:
    - "Vapnik-Chervonenkis 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"

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