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: "Unbiased Assessment of Learning Algorithms"
  Abstract: In order to rank the performance of machine learning algorithms, many researchers conduct experiments on benchmark data sets. Since most learning algorithms have domain-specific parameters, it is a popular custom to adapt these parameters to obtain a minimal error rate on the test set. The same rate is then used to rank the algorithm, which causes an optimistic bias. We quantify this bias, showing, in particular, that an algorithm with more parameters will probably be ranked higher than an equally good algorithm with fewer parameters. We demonstrate this result, showing the number of parameters and trials required in order to pretend to outperform C4.5 or FOIL, respectively, for various benchmark problems. We then describe out how unbiased ranking experiments should be conducted.

Connected through hyperedge HE-1 (induced by source paper "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule"):
  with members:
    - "The out-of-bootstrap method for model averaging and selection"
    - "Classifying Seismic Signals by Integrating Ensembles of Neural Networks"
    - "Flexible Metric Nearest Neighbor Classiflcation"
    - "Combining estimates in regression and classification"
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"
    - "Bias, variance and prediction error for classification rules"
    - "The covariance inflation criterion for adaptive model selection"
    - "Estimating the Accuracy of Learned Concepts"

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