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 Fast Non-Parametric Density Estimation Algorithm"
  Abstract: Non-parametric density estimation is the problem of approximating the values of a probability density function, given samples from the associated distribution. Non-parametric estimation finds applications in discriminant analysis, cluster analysis, and flow calculations based on Smoothed Particle Hydrodynamics. Usual estimators make use of kernel functions, and require on the order of n 2 arithmetic operations to evaluate the density at n sample points. We describe a sequence of special weight functions which requires almost linear number of operations in n for the same computation.

Connected through hyperedge HE-1 (induced by source paper "Parzen. On estimation of a probability density function and mode. Annual Mathematical Statistics, 33:1065-1076, 1962."):
  with members:
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."
    - "Learning networks for face analysis and synthesis"
    - "Flexible Metric Nearest Neighbor Classiflcation"
    - "Learning in neural networks with Bayesian prototypes"
    - "MDL Learning of Probabilistic Neural Networks for Discrete Problem Domains"
    - "Efficient Non-parametric Estimation of Probability Density Functions"

Connected through hyperedge HE-2 (induced by source paper "Efficient Non-parametric Estimation of Probability Density Functions"):
  with members:
    - "Parzen. On estimation of a probability density function and mode. Annual Mathematical Statistics, 33:1065-1076, 1962."

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: Probabilistic_Methods