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: "Learning in the Presence of Prior Knowledge: A Case Study Using Model Calibration"
  Abstract: Computational models of natural systems often contain free parameters that must be set to optimize the predictive accuracy of the models. This process| called calibration|can be viewed as a form of supervised learning in the presence of prior knowledge. In this view, the fixed aspects of the model constitute the prior knowledge, and the goal is to learn values for the free parameters. We report on a series of attempts to learn parameter values for a global vegetation model called MAPSS (Mapped Atmosphere-Plant-Soil System) developed by our collaborator, Ron Neilson. Standard machine learning methods do not work with MAPSS, because the constraints introduced by the structure of the model create a very difficult non-linear optimization problem. We developed a new divide-and-conquer approach in which subsets of the parameters are calibrated while others are held constant. This approach succeeds because it is possible to select training examples that exercise only portions of the model.

Connected through hyperedge HE-1 (induced by source paper "Automated Decomposition of Model-based Learning Problems"):
  with members:
    - "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide"
    - "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"
    - "A Divide-and-Conquer Approach to Learning from Prior Knowledge"

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: Neural_Networks