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: "Integrating Initialization Bias and Search Bias in Neural Network Learning"
  Abstract: The use of previously learned knowledge during learning has been shown to reduce the number of examples required for good generalization, and to increase robustness to noise in the examples. In reviewing various means of using learned knowledge from a domain to guide further learning in the same domain, two underlying classes are discerned. Methods which use previous knowledge to initialize a learner (as an initialization bias), and those that use previous knowledge to constrain a learner (as a search bias). We show such methods in fact exploit the same domain knowledge differently, and can complement each other. This is shown by presenting a combined approach which both initializes and constrains a learner. This combined approach is seen to outperform the individual methods under the conditions that accurate previously learned domain knowledge is available, and that there are irrelevant features in the domain representation.

Connected through hyperedge HE-1 (induced by source paper "Learning Analytically and Inductively"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning"
    - "Theory Refinement Combining Analytical and Empirical Methods"

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