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: "The Functional Transfer of Knowledge for Coronary Artery Disease Diagnosis"
  Abstract: A distinction between two forms of task knowledge transfer, representational and functional, is reviewed followed by a discussion of MTL, a modified version of the multiple task learning (MTL) neural network method of functional transfer. The MTL method employs a separate learning rate, k , for each task output node k. k varies as a function of a measure of relatedness, R k , between the kth task and the primary task of interest. An MTL network is applied to a diagnostic domain of four levels of coronary artery disease. Results of experiments demonstrate the ability of MTL to develop a predictive model for one level of disease which has superior diagnostic ability over models produced by either single task learning or standard multiple task learning.

Connected through hyperedge HE-1 (induced by source paper "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks"):
  with members:
    - "The Efficient Learning of Multiple Task Sequences"
    - "A Modular Q-Learning Architecture for Manipulator Task Decomposition `Data storage in the cerebellar model ar"
    - "A Coevolutionary Approach to Learning Sequential Decision Rules"
    - "Hierarchical Explanation-Based Reinforcement Learning"
    - "Robust Reinforcement Learning in Motion Planning"
    - "Learning to Achieve Goals"
    - "Using Case-Based Reasoning as a Reinforcement Learning Framework for Optimization with Changing Criteria"
    - "Emergent Hierarchical Control Structures: Learning Reactive/Hierarchical Relationships in Reinforcement Environments"

Connected through hyperedge HE-2 (induced by source paper "Learning One More Thing"):
  with members:
    - "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"
    - "Learning Model Bias minimum number of examples requred to learn a single task, and O(a"

Connected through hyperedge HE-3 (induced by source paper "Learning Sequential Tasks by Incrementally Adding Higher Orders"):
  with members:
    - "Induction of Multiscale Temporal Structure"
    - "A Connectionist Symbol Manipulator That Discovers the Structure of Context-Free Languages"
    - "Sequence Learning with Incremental Higher-Order Neural Networks"

Connected through hyperedge HE-4 (induced by source paper "The Task Rehearsal Method of Sequential Learning"):
  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: Neural_Networks