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 Portfolio Selection Using Multiplicative Updates"
  Abstract: We present an on-line investment algorithm which achieves almost the same wealth as the best constant-rebalanced portfolio determined in hindsight from the actual market outcomes. The algorithm employs a multiplicative update rule derived using a framework introduced by Kivinen and Warmuth. Our algorithm is very simple to implement and requires only constant storage and computing time per stock in each trading period. We tested the performance of our algorithm on real stock data from the New York Stock Exchange accumulated during a 22-year period. On this data, our algorithm clearly outperforms the best single stock as well as Cover's universal portfolio selection algorithm. We also present results for the situation in which the investor has access to additional "side information."

Connected through hyperedge HE-1 (induced by source paper "Update rules for parameter estimation in Bayesian networks"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "A Comparison of New and Old Algorithms for A Mixture Estimation Problem"
    - "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide"
    - "A Tutorial on Learning With Bayesian Networks"

Connected through hyperedge HE-2 (induced by source paper "A Comparison of New and Old Algorithms for A Mixture Estimation Problem"):
  with members:
    - "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS"
    - "Training Algorithms for Hidden Markov Models Using Entropy Based Distance Functions"
    - "Update rules for parameter estimation in Bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "Universal Portfolios With and Without Transaction Costs"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"

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