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: "DIFFERENTIALLY GENERATED NEURAL NETWORK CLASSIFIERS ARE EFFICIENT"
  Abstract: Differential learning for statistical pattern classification is described in [5]; it is based on the classification figure-of-merit (CFM) objective function described in [9, 5]. We prove that differential learning is asymptotically efficient, guaranteeing the best generalization allowed by the choice of hypothesis class (see below) as the training sample size grows large, while requiring the least classifier complexity necessary for Bayesian (i.e., minimum probability-of-error) discrimination. Moreover, differential learning almost always guarantees the best generalization allowed by the choice of hypothesis class for small training sample sizes.

Connected through hyperedge HE-1 (induced by source paper "DIAGNOSING AND CORRECTING SYSTEM ANOMALIES WITH A ROBUST CLASSIFIER"):
  with members:
    - "Differential theory of learning for efficient neural network pattern recognition"

Connected through hyperedge HE-2 (induced by source paper "Differential theory of learning for efficient neural network pattern recognition"):
  with members:
    - "Learning networks for face analysis and synthesis"
    - "DIAGNOSING AND CORRECTING SYSTEM ANOMALIES WITH A ROBUST CLASSIFIER"

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