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: "Hierarchical Mixtures of Experts and the EM Algorithm"
  Abstract: We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain. This report describes research done at the Dept. of Brain and Cognitive Sciences, the Center for Biological and Computational Learning, and the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for CBCL is provided in part by a grant from the NSF (ASC-9217041). Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Dept. of Defense. The authors were supported by a grant from the McDonnell-Pew Foundation, by a grant from ATR Human Information Processing Research Laboratories, by a grant from Siemens Corporation, by by grant IRI-9013991 from the National Science Foundation, by grant N00014-90-J-1942 from the Office of Naval Research, and by NSF grant ECS-9216531 to support an Initiative in Intelligent Control at MIT. Michael I. Jordan is a NSF Presidential Young Investigator.

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."
    - "Avoiding overfitting by locally matching the noise level of the data gating network discovers the"
    - "Nonlinear gated experts for time series: discovering regimes and avoiding overfitting"

Connected through hyperedge HE-2 (induced by source paper "Combining Linear Discriminant Functions with Neural Networks for Supervised Learning"):
  with members:
    - "Constructive Training Methods for Feedforward Neural Networks with Binary Weights"

Connected through hyperedge HE-3 (induced by source paper "Factorial Learning and the EM Algorithm"):
  with members:
    - "Unsupervised Learning by Convex and Conic Coding"

Connected through hyperedge HE-4 (induced by source paper "Expectation-Based Selective Attention for Visual Monitoring and Control of a Robot Vehicle"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"

Connected through hyperedge HE-5 (induced by source paper "A Modular Q-Learning Architecture for Manipulator Task Decomposition `Data storage in the cerebellar model ar"):
  with members:
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "The Efficient Learning of Multiple Task Sequences"
    - "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks"
    - "Exploration and Model Building in Mobile Robot Domains"

Connected through hyperedge HE-6 (induced by source paper "Constructive Algorithms for Hierarchical Mixtures of Experts"):
  with members:
    - "CLASSIFICATION USING HIERARCHICAL MIXTURES OF EXPERTS"

Connected through hyperedge HE-7 (induced by source paper "Classifying Seismic Signals by Integrating Ensembles of Neural Networks"):
  with members:
    - "Combining estimates in regression and classification"
    - "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule"

Connected through hyperedge HE-8 (induced by source paper "Non-linear Models for Time Series Using Mixtures of Experts"):
  with members:
    - "On Convergence of the EM Algorithm and the Gibbs Sampler  SUMMARY"
    - "Nonlinear gated experts for time series: discovering regimes and avoiding overfitting"

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