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: "Neural Networks with Quadratic VC Dimension"
  Abstract: This paper shows that neural networks which use continuous activation functions have VC dimension at least as large as the square of the number of weights w. This result settles a long-standing open question, namely whether the well-known O(w log w) bound, known for hard-threshold nets, also held for more general sigmoidal nets. Implications for the number of samples needed for valid generalization are discussed.

Connected through hyperedge HE-1 (induced by source paper "Similar Classifiers and VC Error Bounds"):
  with members:
    - "Improved Uniform Test Error Bounds"
    - "Partition-Based Uniform Error Bounds"

Connected through hyperedge HE-2 (induced by source paper "Shattering all sets of k points in "general position" requires (k 1)=2 parameters"):
  with members:
    - "Critical Points for Least-Squares Problems Involving Certain Analytic Functions, with Applications to Sigmoidal Nets"
    - "Networks of Spiking Neurons: The Third Generation of Neural Network Models"

Connected through hyperedge HE-3 (induced by source paper "Vapnik-Chervonenkis Dimension of Recurrent Neural Networks"):
  with members:
    - "Sample Complexity for Learning Recurrent Perceptron Mappings"
    - "NEURAL NETS AS SYSTEMS MODELS AND CONTROLLERS suitability of "neural nets" as models for dynamical"
    - "On the Effect of Analog Noise in Discrete-Time Analog Computations"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "Dimension of Recurrent Neural Networks"):
  with members:
    - "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"

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