human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Differential theory of learning for efficient neural network pattern recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Learning networks for face analysis and synthesis")
  Member "DIAGNOSING AND CORRECTING SYSTEM ANOMALIES WITH A ROBUST CLASSIFIER":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Differential theory of learning for efficient neural network pattern recognition")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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