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: "Partition-Based Uniform Error Bounds"
  Abstract: This paper develops probabilistic bounds on out-of-sample error rates for several classifiers using a single set of in-sample data. The bounds are based on probabilities over partitions of the union of in-sample and out-of-sample data into in-sample and out-of-sample data sets. The bounds apply when in-sample and out-of-sample data are drawn from the same distribution. Partition-based bounds are stronger than VC-type bounds, but they require more computation.

Connected through hyperedge HE-1 (induced by source paper "Improved Hoeffding-Style Performance Guarantees for Accurate Classifiers"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Similar Classifiers and VC Error Bounds"):
  with members:
    - "Neural Networks with Quadratic VC Dimension"
    - "Improved Uniform Test Error Bounds"

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