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 Model Bias minimum number of examples requred to learn a single task, and O(a"
  Abstract: In this paper the problem of learning appropriate domain-specific bias is addressed. It is shown that this can be achieved by learning many related tasks from the same domain, and a theorem is given bounding the number tasks that must be learnt. A corollary of the theorem is that if the tasks are known to possess a common internal representation or preprocessing then the number of examples required per task for good generalisation when learning n tasks simultaneously scales like O(a + b tive support for the theoretical results is reported.

Connected through hyperedge HE-1 (induced by source paper "Learning One More Thing"):
  with members:
    - "The Functional Transfer of Knowledge for Coronary Artery Disease Diagnosis"
    - "Incremental Class Learning approach and its application to Handwritten Digit Recognition"
    - "Recognition and Exploitation of Contextual Clues via Incremental Meta-Learning (Extended Version)"
    - "The Canonical Distortion Measure for Vector Quantization and Function Approximation"

Connected through hyperedge HE-2 (induced by source paper "Theoretical Models of Learning to Learn  Editor:"):
  with members:
    (none)

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: Theory