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 correct 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. Unfortunately, attempts to apply standard machine learning methods|specifically global error functions and gradient descent search|do not work with MAPSS, because the constraints introduced by the structure of the model (the prior knowledge) create a very difficult non-linear optimization problem. Successful calibration of MAPSS required taking a divide-and-conquer approach in which subsets of the parameters were calibrated while others were held constant. This approach was made possible by carefully selecting training sets that exercised only portions of the model and by designing error functions for each part that had desirable properties. The automated calibration tool that we have developed is currently being applied to calibrate MAPSS against a global climate data set.

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"

Connected through hyperedge HE-2 (induced by source paper "Quantifying Prior Determination Knowledge using the PAC Learning Model"):
  with members:
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "Finding new rules for incomplete theories: Explicit biases for induction with contextual information. In Proceedings"
    - "Learning from Queries and Examples with Tree-structured Bias"
    - "A Formalization of Explanation-Based Macro-operator Learning"

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