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: "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide"
  Abstract: This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical specification. This includes versions of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper concludes by sketching some implications for data analysis and summarizing how some popular algorithms fall within the framework presented.

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"
    - "A Tutorial on Learning With Bayesian Networks"
    - "On-Line Portfolio Selection Using Multiplicative Updates"

Connected through hyperedge HE-2 (induced by source paper "Automated Decomposition of Model-based Learning Problems"):
  with members:
    - "Global Conditioning for Probabilistic Inference in Belief Networks"
    - "A Tutorial on Learning With Bayesian Networks"
    - "on Qualitative Reasoning about Physical Systems  Deriving Monotonic Function Envelopes from Observations"
    - "Learning in the Presence of Prior Knowledge: A Case Study Using Model Calibration"
    - "Learning in the Presence of Prior Knowledge: A Case Study Using Model Calibration"
    - "A Divide-and-Conquer Approach to Learning from Prior Knowledge"

Connected through hyperedge HE-3 (induced by source paper "Robustness Analysis of Bayesian Networks with Finitely Generated Convex Sets of Distributions"):
  with members:
    - "BUCKET ELIMINATION: A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE"
    - "Exploiting Causal Independence in Bayesian Network Inference"
    - "Using Qualitative Relationships for Bounding Probability Distributions"
    - "Robustness Analysis of Bayesian Networks with Global Neighborhoods"

Connected through hyperedge HE-4 (induced by source paper "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View"):
  with members:
    - "Exploiting Tractable Substructures in Intractable Networks"
    - "Probabilistic Independence Networks for Hidden Markov Probability Models"

Connected through hyperedge HE-5 (induced by source paper "Chain graphs for learning"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "On the Markov Equivalence of Chain Graphs, Undirected Graphs, and Acyclic Digraphs"

Connected through hyperedge HE-6 (induced by source paper "Discovering Structure in Continuous Variables Using Bayesian Networks"):
  with members:
    - "Continuous sigmoidal belief networks trained using slice sampling"
    - "A Tutorial on Learning With Bayesian Networks"

Connected through hyperedge HE-7 (induced by source paper "Learning Limited Dependence Bayesian Classifiers"):
  with members:
    - "Building Classifiers using Bayesian Networks"
    - "Toward Optimal Feature Selection"
    - "Searching for dependencies in Bayesian classifiers j A n V n j If the attributes"

Connected through hyperedge HE-8 (induced by source paper "Mean Field Theory for Sigmoid Belief Networks"):
  with members:
    - "Large Deviation Methods for Approximate Probabilistic Inference, with Rates of Convergence a free parameter. The"
    - "GIBBS-MARKOV MODELS"
    - "GIBBS-MARKOV MODELS"
    - "GIBBS-MARKOV MODELS"
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Learning Generative Models with the Up-Propagation Algorithm"
    - "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS"
    - "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