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: "Similar Classifiers and VC Error Bounds"
  Abstract: We improve error bounds based on VC analysis for classes with sets of similar classifiers. We apply the new error bounds to separating planes and artificial neural networks. Key words machine learning, learning theory, generalization, Vapnik-Chervonenkis, separating planes, neural networks.

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

Connected through hyperedge HE-2 (induced by source paper "Partition-Based Uniform Error Bounds"):
  with members:
    - "Improved Uniform Test Error Bounds"
    - "Improved Hoeffding-Style Performance Guarantees for Accurate Classifiers"

Connected through hyperedge HE-3 (induced by source paper "Improved Uniform Test Error Bounds"):
  with members:
    - "Partition-Based Uniform 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: Theory