human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Update rules for parameter estimation in Bayesian networks")
  Member "Global Conditioning for Probabilistic Inference in Belief Networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Topological Parameters for time-space tradeoff")
  Member "A Tutorial on Learning With Bayesian Networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Update rules for parameter estimation in Bayesian networks")
  Member "on Qualitative Reasoning about Physical Systems  Deriving Monotonic Function Envelopes from Observations":
    also connected through other hyperedges:
      (none)
  Member "Learning in the Presence of Prior Knowledge: A Case Study Using Model Calibration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Quantifying Prior Determination Knowledge using the PAC Learning Model")
  Member "Learning in the Presence of Prior Knowledge: A Case Study Using Model Calibration":
    also connected through other hyperedges:
      (none)
  Member "A Divide-and-Conquer Approach to Learning from Prior Knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Using Qualitative Models to Guide Inductive Learning")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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