human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "A Comparison of New and Old Algorithms for A Mixture Estimation Problem"):
  Abstract: We investigate the problem of estimating the proportion vector which maximizes the likelihood of a given sample for a mixture of given densities. We adapt a framework developed for supervised learning and give simple derivations for many of the standard iterative algorithms like gradient projection and EM. In this framework, the distance between the new and old proportion vectors is used as a penalty term. The square distance leads to the gradient projection update, and the relative entropy to a new update which we call the exponentiated gradient update (EG ). Curiously, when a second order Taylor expansion of the relative entropy is used, we arrive at an update EM which, for = 1, gives the usual EM update. Experimentally, both the EM -update and the EG -update for &gt; 1 outperform the EM algorithm and its variants. We also prove a polynomial bound on the rate of convergence of the EG algorithm.

Includes members:
  Member "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Free energy coding  In")
  Member "Training Algorithms for Hidden Markov Models Using Entropy Based Distance Functions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "On the Learnability and Usage of Acyclic Probabilistic Finite Automata")
  Member "Update rules for parameter estimation in Bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide")
  Member "On-Line Portfolio Selection Using Multiplicative Updates":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Update rules for parameter estimation in Bayesian networks")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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