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 Bounds about On-line Learning of Smooth Functions of a Single Variable"
  Abstract: We consider the complexity of learning classes of smooth functions formed by bounding different norms of a function's derivative. The learning model is the generalization of the mistake-bound model to continuous-valued functions. Suppose F q is the set of all absolutely continuous functions f from [0; 1] to R such that jjf 0 jj q 1, and opt(F q ; m) is the best possible bound on the worst-case sum of absolute prediction errors over sequences of m trials. We show that for all q 2, opt(F q ; m) = fi(

Connected through hyperedge HE-1 (induced by source paper "ON-LINE LEARNING OF LINEAR FUNCTIONS"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "Worst-case Quadratic Loss Bounds for Prediction Using Linear Functions and Gradient Descent"

Connected through hyperedge HE-2 (induced by source paper "Worst-case Quadratic Loss Bounds for Prediction Using Linear Functions and Gradient Descent"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "ON-LINE LEARNING OF LINEAR FUNCTIONS"

Connected through hyperedge HE-3 (induced by source paper "On the Complexity of Function Learning"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "Simulating Access to Hidden Information while Learning"
    - "Toward Efficient Agnostic 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