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 Neural Network Model for Prognostic Prediction"
  Abstract: An important and difficult prediction task in many domains, particularly medical decision making, is that of prognosis. Prognosis presents a unique set of problems to a learning system when some of the outputs are unknown. This paper presents a new approach to prognostic prediction, using ideas from nonparametric statistics to fully utilize all of the available information in a neural architecture. The technique is applied to breast cancer prognosis, resulting in flexible, accurate models that may play a role in prevent ing unnecessary surgeries.

Connected through hyperedge HE-1 (induced by source paper "An Inductive Learning Approach to Prognostic Prediction"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Individual and Collective Prognostic Prediction"

Connected through hyperedge HE-2 (induced by source paper "Individual and Collective Prognostic Prediction"):
  with members:
    - "Feature Selection via Mathematical Programming"
    - "An Inductive Learning Approach to Prognostic Prediction"

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