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 OF LINEAR FUNCTIONS"
  Abstract: We present an algorithm for the on-line learning of linear functions which is optimal to within a constant factor with respect to bounds on the sum of squared errors for a worst case sequence of trials. The bounds are logarithmic in the number of variables. Furthermore, the algorithm is shown to be optimally robust with respect to noise in the data (again to within a constant factor). Key words. Machine learning; computational learning theory; on-line learning; linear functions; worst-case loss bounds; adaptive filter theory. Subject classifications. 68T05.

Connected through hyperedge HE-1 (induced by source paper "Improved Bounds about On-line Learning of Smooth Functions of a Single Variable"):
  with members:
    - "Worst-case Quadratic Loss Bounds for Prediction Using Linear Functions and Gradient Descent"
    - "On the Complexity of Function Learning"

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)"
    - "Improved Bounds about On-line Learning of Smooth Functions of a Single Variable"

Connected through hyperedge HE-3 (induced by source paper "How to Use Expert Advice (Extended Abstract)"):
  with members:
    - "Online Learning versus O*ine Learning"
    - "Simulating Access to Hidden Information while Learning"
    - "Toward Efficient Agnostic Learning"
    - "Efficient Distribution-free Learning of Probabilistic Concepts"
    - "Context-sensitive learning methods for text categorization"
    - "WORST CASE PREDICTION OVER SEQUENCES UNDER LOG LOSS"
    - "Large Margin Classification Using the Perceptron Algorithm"
    - "An Efficient Extension to Mixture Techniques for Prediction and Decision Trees"

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