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: "On-line Learning with Linear Loss Constraints"
  Abstract: We consider a generalization of the mistake-bound model (for learning f0; 1g-valued functions) in which the learner must satisfy a general constraint on the number M + of incorrect 1 predictions and the number M of incorrect 0 predictions. We describe a general-purpose optimal algorithm for our formulation of this problem. We describe several applications of our general results, involving situations in which the learner wishes to satisfy linear inequalities in M + and M .

Connected through hyperedge HE-1 (induced by source paper "Apple Tasting and Nearly One-Sided Learning"):
  with members:
    - "Online Learning versus O*ine Learning"
    - "Sequential PAC 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