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: "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.

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)"
    - "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide"
    - "A Tutorial on Learning With Bayesian Networks"
    - "On-Line Portfolio Selection Using Multiplicative Updates"

Connected through hyperedge HE-2 (induced by source paper "On-Line Portfolio Selection Using Multiplicative Updates"):
  with members:
    - "Universal Portfolios With and Without Transaction Costs"
    - "Update rules for parameter estimation in Bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "Training Algorithms for Hidden Markov Models Using Entropy Based Distance Functions"):
  with members:
    - "On Convergence Properties of the EM Algorithm for Gaussian Mixtures"
    - "On the Learnability and Usage of Acyclic Probabilistic Finite Automata"

Connected through hyperedge HE-4 (induced by source paper "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS"):
  with members:
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "A HIERARCHICAL COMMUNITY OF EXPERTS"
    - "A Statistical Approach to Decision Tree Modeling"
    - "On Convergence Properties of the EM Algorithm for Gaussian Mixtures"
    - "Efficient Stochastic Source Coding and an Application to a Bayesian Network Source Model"
    - "On Structured Variational Approximations"
    - "The Expectation-Maximization Algorithm for MAP Estimation"
    - "Localist Attractor Networks"

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