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: "Improved Uniform Test Error Bounds"
  Abstract: We derive distribution-free uniform test error bounds that improve on VC-type bounds for validation. We show how to use knowledge of test inputs to improve the bounds. The bounds are sharp, but they require intense computation. We introduce a method to trade sharpness for speed of computation. Also, we compute the bounds for several test cases.

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

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

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