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 "Differential theory of learning for efficient neural network pattern recognition"):
  Abstract: We describe a new theory of differential learning by which a broad family of pattern classifiers (including many well-known neural network paradigms) can learn stochastic concepts efficiently. We describe the relationship between a classifier's ability to generalize well to unseen test examples and the efficiency of the strategy by which it learns. We list a series of proofs that differential learning is efficient in its information and computational resource requirements, whereas traditional probabilistic learning strategies are not. The proofs are illustrated by a simple example that lends itself to closed-form analysis. We conclude with an optical character recognition task for which three different types of differentially generated classifiers generalize significantly better than their probabilistically generated counterparts.

Includes members:
  Member "Learning networks for face analysis and synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Receptive Fields for Vision: from Hyperacuity to Object Recognition")
  Member "DIAGNOSING AND CORRECTING SYSTEM ANOMALIES WITH A ROBUST CLASSIFIER":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "DIFFERENTIALLY GENERATED NEURAL NETWORK CLASSIFIERS ARE EFFICIENT")
  Member "DIFFERENTIALLY GENERATED NEURAL NETWORK CLASSIFIERS ARE EFFICIENT":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "DIAGNOSING AND CORRECTING SYSTEM ANOMALIES WITH A ROBUST CLASSIFIER")

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