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: "Automated Decomposition of Model-based Learning Problems"
  Abstract: A new generation of sensor rich, massively distributed autonomous systems is being developed that has the potential for unprecedented performance, such as smart buildings, reconfigurable factories, adaptive traffic systems and remote earth ecosystem monitoring. To achieve high performance these massive systems will need to accurately model themselves and their environment from sensor information. Accomplishing this on a grand scale requires automating the art of large-scale modeling. This paper presents a formalization of decompositional, model-based learning (DML), a method developed by observing a modeler's expertise at decomposing large scale model estimation tasks. The method exploits a striking analogy between learning and consistency-based diagnosis. Moriarty, an implementation of DML, has been applied to thermal modeling of a smart building, demonstrating a significant improvement in learning rate.

Connected through hyperedge HE-1 (induced by source paper "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide"):
  with members:
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Robustness Analysis of Bayesian Networks with Finitely Generated Convex Sets of Distributions"
    - "Chain graphs for learning"
    - "Learning Limited Dependence Bayesian Classifiers"
    - "Update rules for parameter estimation in Bayesian networks"
    - "Discovering Structure in Continuous Variables Using Bayesian Networks"
    - "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View"

Connected through hyperedge HE-2 (induced by source paper "on Qualitative Reasoning about Physical Systems  Deriving Monotonic Function Envelopes from Observations"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "A Divide-and-Conquer Approach to Learning from Prior Knowledge"):
  with members:
    - "Using Qualitative Models to Guide Inductive Learning"

Connected through hyperedge HE-4 (induced by source paper "Learning in the Presence of Prior Knowledge: A Case Study Using Model Calibration"):
  with members:
    - "Quantifying Prior Determination Knowledge using the PAC Learning Model"

Connected through hyperedge HE-5 (induced by source paper "Global Conditioning for Probabilistic Inference in Belief Networks"):
  with members:
    - "BUCKET ELIMINATION: A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE"
    - "Structured Representation of Complex Stochastic Systems"
    - "Topological Parameters for time-space tradeoff"
    - "Logarithmic Time Parallel Bayesian Inference"

Connected through hyperedge HE-6 (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"
    - "Sequential Update of Bayesian Network Structure"
    - "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"

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