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: "Training Algorithms for Hidden Markov Models Using Entropy Based Distance Functions"
  Abstract: We present new algorithms for parameter estimation of HMMs. By adapting a framework used for supervised learning, we construct iterative algorithms that maximize the likelihood of the observations while also attempting to stay close to the current estimated parameters. We use a bound on the relative entropy between the two HMMs as a distance measure between them. The result is new iterative training algorithms which are similar to the EM (Baum-Welch) algorithm for training HMMs. The proposed algorithms are composed of a step similar to the expectation step of Baum-Welch and a new update of the parameters which replaces the maximization (re-estimation) step. The algorithm takes only negligibly more time per iteration and an approximated version uses the same expectation step as Baum-Welch. We evaluate experimentally the new algorithms on synthetic and natural speech pronunciation data. For sparse models, i.e. models with relatively small number of non-zero parameters, the proposed algorithms require significantly fewer iterations.

Connected through hyperedge HE-1 (induced by source paper "On the Learnability and Usage of Acyclic Probabilistic Finite Automata"):
  with members:
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "Learning Probabilistic Automata with Variable Memory Length"
    - "Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)"
    - "On Learning Bounded-Width Branching Programs"

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"
    - "Update rules for parameter estimation in Bayesian networks"
    - "On-Line Portfolio Selection Using Multiplicative Updates"

Connected through hyperedge HE-3 (induced by source paper "On Convergence Properties of the EM Algorithm for Gaussian Mixtures"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian"
    - "CLASSIFICATION USING HIERARCHICAL MIXTURES OF EXPERTS"
    - "On Convergence of the EM Algorithm and the Gibbs Sampler  SUMMARY"
    - "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS"
    - "On Computing the Largest Fraction of Missing Information for the EM Algorithm and the Worst"

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