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 "Parzen. On estimation of a probability density function and mode. Annual Mathematical Statistics, 33:1065-1076, 1962."):
  Abstract: To apply the algorithm for classification we assign each class a separate set of codebook Gaussians. Each set is only trained with patterns from a single class. After having trained the codebook Gaussians, each set provides an estimate of the probability function of one class; just as with Parzen window estimation, we take as the estimate of the pattern distribution the average of all Gaussians in the set. Classification of a pattern may now be done by calculating the probability of each class at the respective sample point, and assigning to the pattern the class with the highest probability. Hence the whole codebook plays a role in the classification of patterns. This is not the case with regular classification schemes using codebooks. We have tested the classification scheme on several classification tasks including the two spiral problem. We compared our algorithm to various other classification algorithms and it came out second; the best algorithm for the applications is the Parzen window estimation. However, the computing time and memory for Parzen window estimation are excessive when compared to our algorithm, and hence, in practical situations, our algorithm is to be preferred. We have developed a fast algorithm which combines attractive properties of both Parzen window estimation and vector quantization. The scale parameter is tuned adaptively and, therefore, is not set in an ad hoc manner. It allows a classification strategy in which all the codebook vectors are taken into account. This yields better results than the standard vector quantization techniques. An interesting topic for further research is to use radially non-symmetric Gaussians.

Includes members:
  Member "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex.":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "LEARNING TO AVOID COLLISIONS: A REINFORCEMENT LEARNING PARADIGM FOR MOBILE ROBOT NAVIGATION")
  Member "Learning networks for face analysis and synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Receptive Fields for Vision: from Hyperacuity to Object Recognition")
  Member "Flexible Metric Nearest Neighbor Classiflcation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Selection of Relevant Features and Examples in Machine Learning")
  Member "Learning in neural networks with Bayesian prototypes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Bayesian Case-Based Reasoning with Neural Networks")
  Member "MDL Learning of Probabilistic Neural Networks for Discrete Problem Domains":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Bayesian Case-Based Reasoning with Neural Networks")
  Member "A Fast Non-Parametric Density Estimation Algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Efficient Non-parametric Estimation of Probability Density Functions")
  Member "Efficient Non-parametric Estimation of Probability Density Functions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "A Fast Non-Parametric Density Estimation Algorithm")

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