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: "A Divide-and-Conquer Approach to Learning from Prior Knowledge"
  Abstract: This paper introduces a new machine learning task|model calibration|and presents a method for solving a particularly difficult model calibration task that arose as part of a global climate change research project. The model calibration task is the problem of training the free parameters of a scientific model in order to optimize the accuracy of the model for making future predictions. It is a form of supervised learning from examples in the presence of prior knowledge. An obvious approach to solving calibration problems is to formulate them as global optimization problems in which the goal is to find values for the free parameters that minimize the error of the model on training data. Unfortunately, this global optimization approach becomes computationally infeasible when the model is highly nonlinear. This paper presents a new divide-and-conquer method that analyzes the model to identify a series of smaller optimization problems whose sequential solution solves the global calibration problem. This paper argues that methods of this kind|rather than global optimization techniques|will be required in order for agents with large amounts of prior knowledge to learn efficiently.

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"
    - "Learning in the Presence of Prior Knowledge: A Case Study Using Model Calibration"

Connected through hyperedge HE-2 (induced by source paper "Using Qualitative Models to Guide Inductive Learning"):
  with members:
    - "Rule Induction with CN2: Some Recent Improvements"
    - "Rerepresenting and Restructuring Domain Theories: A Constructive Induction Approach"

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