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: "Trees and Splines in Survival Analysis"
  Abstract: Technical Report No. 275 Revised March 30, 1995 University of Washington Department of Statistics Seattle, Washington 98195 Abstract During the past few years several nonparametric alternatives to the Cox proportional hazards model have appeared in the literature. These methods extend techniques that are well known from regression analysis to the analysis of censored survival data. In this paper we discuss methods based on (partition) trees and (polynomial) splines, analyze two datasets using both Survival Trees[1] and HARE[2], and compare the strengths and weaknesses of the two methods. One of the strengths of HARE is that its model fitting procedure has an implicit check for proportionality of the underlying hazards model. It also provides an explicit model for the conditional hazards function, which makes it very convenient to obtain graphical summaries. On the other hand, the tree-based methods automatically partition a dataset into groups of cases that are similar in survival history. Results obtained by survival trees and HARE are often complimentary. Trees and splines in survival analysis should provide the data analyst with two useful tools when analyzing survival data.

Connected through hyperedge HE-1 (induced by source paper "Using the Stochastic Gradient Method to Fit Polychotomous Regression Models"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"

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: Neural_Networks