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: "Efficient Non-parametric Estimation of Probability Density Functions"
  Abstract: Accurate and fast estimation of probability density functions is crucial for satisfactory computational performance in many scientific problems. When the type of density is known a priori, then the problem becomes statistical estimation of parameters from the observed values. In the non-parametric case, usual estimators make use of kernel functions. If X j ; j = 1; 2; : : : ; n is a sequence of i.i.d. random variables with estimated probability density function f n , in the kernel method the computation of the values f n (X 1 ); f n (X 2 ); : : : ; f n (X n ) requires O(n 2 ) operations, since each kernel needs to be evaluated at every X j . We propose a sequence of special weight functions for the non-parametric estimation of f which requires almost linear time: if m is a slowly growing function that increases without bound with n, our method requires only O(m 2 n) arithmetic operations. We derive conditions for convergence under a number of metrics, which turn out to be similar to those required for the convergence of kernel based methods. We also discuss experiments on different distributions and compare the efficiency and the accuracy of our computations with kernel based estimators for various values of n and m.

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"
    - "A Fast Non-Parametric Density Estimation Algorithm"

Connected through hyperedge HE-2 (induced by source paper "A Fast Non-Parametric Density Estimation Algorithm"):
  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