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: "Nonlinear gated experts for time series: discovering regimes and avoiding overfitting"
  Abstract: In: International Journal of Neural Systems 6 (1995) p. 373 - 399. URL of this paper: ftp://ftp.cs.colorado.edu/pub/Time-Series/MyPapers/experts.ps.Z, or http://www.cs.colorado.edu/~andreas/Time-Series/MyPapers/experts.ps.Z University of Colorado Computer Science Technical Report CU-CS-798-95. In the analysis and prediction of real-world systems, two of the key problems are nonstationarity(often in the form of switching between regimes), and overfitting (particularly serious for noisy processes). This article addresses these problems using gated experts, consisting of a (nonlinear) gating network, and several (also nonlinear) competing experts. Each expert learns to predict the conditional mean, and each expert adapts its width to match the noise level in its regime. The gating network learns to predict the probability of each expert, given the input. This article focuses on the case where the gating network bases its decision on information from the inputs. This can be contrasted to hidden Markov models where the decision is based on the previous state(s) (i.e., on the output of the gating network at the previous time step), as well as to averaging over several predictors. In contrast, gated experts soft-partition the input space. This article discusses the underlying statistical assumptions, derives the weight update rules, and compares the performance of gated experts to standard methods on three time series: (1) a computer-generated series, obtained by randomly switching between two nonlinear processes, (2) a time series from the Santa Fe Time Series Competition (the light intensity of a laser in chaotic state), and (3) the daily electricity demand of France, a real-world multivariate problem with structure on several time scales. The main results are (1) the gating network correctly discovers the different regimes of the process, (2) the widths associated with each expert are important for the segmentation task (and they can be used to characterize the sub-processes), and (3) there is less overfitting compared to single networks (homogeneous multi-layer perceptrons), since the experts learn to match their variances to the (local) noise levels. This can be viewed as matching the local complexity of the model to the local complexity of the data.

Connected through hyperedge HE-1 (induced by source paper "Forecasting electricity demand using nonlinear mixture of experts"):
  with members:
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Avoiding overfitting by locally matching the noise level of the data gating network discovers the"

Connected through hyperedge HE-2 (induced by source paper "Non-linear Models for Time Series Using Mixtures of Experts"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "On Convergence of the EM Algorithm and the Gibbs Sampler  SUMMARY"

Connected through hyperedge HE-3 (induced by source paper "Introduction to Radial Basis Function Networks"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Neural Networks and Statistical Models Proceedings of the Nineteenth Annual SAS Users Group International Conference,"
    - "Growing Cell Structures A Self-organizing Network for Unsupervised and Supervised Learning"

Connected through hyperedge HE-4 (induced by source paper "NONLINEAR TRADING MODELS THROUGH SHARPE RATIO MAXIMIZATION"):
  with members:
    - "TO IMPROVE FORECASTING"
    - "``Learning Local Error Bars for Nonlinear Regression.''  Learning Local Error Bars for Nonlinear Regression"

Connected through hyperedge HE-5 (induced by source paper "Segmenting Time Series using Gated Experts with Simulated Annealing"):
  with members:
    - "Annealed Competition of Experts for a Segmentation and Classification of Switching Dynamics"

Connected through hyperedge HE-6 (induced by source paper "Modeling Volatility using State Space Models"):
  with members:
    - "Learning networks for face analysis and synthesis"
    - "Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines"
    - "TO IMPROVE FORECASTING"
    - "A Bootstrap Evaluation of the Effect of Data Splitting on Financial Time Series"

Connected through hyperedge HE-7 (induced by source paper "First experiments using a mixture of nonlinear experts for time series prediction"):
  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