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: "Virtual Seens and the Frequently Used Dataset"
  Abstract: The paper considers the situation in which a learner's testing set contains close approximations of cases which appear in the training set. Such cases can be considered `virtual seens' since they are approximately seen by the learner. Generalisation measures which do not take account of the frequency of virtual seens may be misleading. The paper shows that the 1-NN algorithm can be used to derive a normalising baseline for gen-eralisation statistics. The normalisation process is demonstrated though application to Holte's [1] study in which the generalisation performance of the 1R algorithm was tested against C4.5 on 16 commonly used datasets.

Connected through hyperedge HE-1 (induced by source paper "Flexible Metric Nearest Neighbor Classiflcation"):
  with members:
    - "Adaptive Noise Injection for Input Variables Relevance Determination"
    - "An adaptation of Relief for attribute estimation in regression"
    - "Parzen. On estimation of a probability density function and mode. Annual Mathematical Statistics, 33:1065-1076, 1962."
    - "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule"
    - "DART/HYESS Users Guide  recursive covering approach to local learning"
    - "Selection of Relevant Features and Examples in Machine Learning"

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