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 Canonical Distortion Measure for Vector Quantization and Function Approximation"
  Abstract: To measure the quality of a set of vector quantization points a means of measuring the distance between a random point and its quantization is required. Common metrics such as the Hamming and Euclidean metrics, while mathematically simple, are inappropriate for comparing natural signals such as speech or images. In this paper it is shown how an environment of functions on an input space X induces a canonical distortion measure (CDM) on X. The depiction canonical is justified because it is shown that optimizing the reconstruction error of X with respect to the CDM gives rise to optimal piecewise constant approximations of the functions in the environment. The CDM is calculated in closed form for several different function classes. An algorithm for training neural networks to implement the CDM is presented along with some en couraging experimental results.

Connected through hyperedge HE-1 (induced by source paper "The Canonical Distortion Measure in Feature Space and 1-NN Classification"):
  with members:
    (none)

Connected through hyperedge HE-2 (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)"
    - "Learning Model Bias minimum number of examples requred to learn a single task, and O(a"

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