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: "Sequential Update of Bayesian Network Structure"
  Abstract: There is an obvious need for improving the performance and accuracy of a Bayesian network as new data is observed. Because of errors in model construction and changes in the dynamics of the domains, we cannot afford to ignore the information in new data. While sequential update of parameters for a fixed structure can be accomplished using standard techniques, sequential update of network structure is still an open problem. In this paper, we investigate sequential update of Bayesian networks were both parameters and structure are expected to change. We introduce a new approach that allows for the flexible manipulation of the tradeoff between the quality of the learned networks and the amount of information that is maintained about past observations. We formally describe our approach including the necessary modifications to the scoring functions for learning Bayesian networks, evaluate its effectiveness through and empirical study, and extend it to the case of missing data.

Connected through hyperedge HE-1 (induced by source paper "Learning Belief Networks in the Presence of Missing Values and Hidden Variables"):
  with members:
    - "A Tutorial on Learning With Bayesian Networks"

Connected through hyperedge HE-2 (induced by source paper "Generalized Prioritized Sweeping"):
  with members:
    - "Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "Tight Performance Bounds on Greedy Policies Based on Imperfect Value Functions"
    - "A Tutorial on Learning With Bayesian Networks"

Connected through hyperedge HE-3 (induced by source paper "LEARNING BAYESIAN NETWORKS WITH LOCAL STRUCTURE"):
  with members:
    - "Context-Specific Independence in Bayesian Networks"
    - "Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks"
    - "A Tutorial on Learning With Bayesian Networks"
    - "On the Sample Complexity of Learning Bayesian Networks"

Connected through hyperedge HE-4 (induced by source paper "A Tutorial on Learning With Bayesian Networks"):
  with members:
    - "LEARNING BAYESIAN NETWORKS WITH LOCAL STRUCTURE"
    - "Learning Bayesian Networks from Incomplete Data"
    - "Automated Decomposition of Model-based Learning Problems"
    - "Bayesian Finite Mixtures for Nonlinear Modeling of Educational data"
    - "On the Sample Complexity of Learning Bayesian Networks"
    - "Update rules for parameter estimation in Bayesian networks"
    - "Discovering Structure in Continuous Variables Using Bayesian Networks"
    - "Learning Belief Networks in the Presence of Missing Values and Hidden Variables"

Connected through hyperedge HE-5 (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: Probabilistic_Methods