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: "In:  A Mixture Model System for Medical and Machine Diagnosis"
  Abstract: Diagnosis of human disease or machine fault is a missing data problem since many variables are initially unknown. Additional information needs to be obtained. The joint probability distribution of the data can be used to solve this problem. We model this with mixture models whose parameters are estimated by the EM algorithm. This gives the benefit that missing data in the database itself can also be handled correctly. The request for new information to refine the diagnosis is performed using the maximum utility principle. Since the system is based on learning it is domain independent and less labor intensive than expert systems or probabilistic networks. An example using a heart disease database is presented.

Connected through hyperedge HE-1 (induced by source paper "Information-based objective functions for active data selection"):
  with members:
    - "In  Active Learning with Statistical Models"
    - "A Practical Bayesian Framework for Backprop Networks"
    - "Heterogeneous Uncertainty Sampling for Supervised Learning"
    - "Improving Generalization with Active Learning"
    - "REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NON-DETERMINISTIC ENVIRONMENTS"
    - "Advances in Neural Information Processing Systems 8 Active Learning in Multilayer Perceptrons"
    - "An Algorithm for Active Data Collection for Learning Feasibility Study with Neural Networks."

Connected through hyperedge HE-2 (induced by source paper "Neural Network Exploration Using Optimal Experiment Design"):
  with members:
    - "In  Active Learning with Statistical Models"
    - "Finding Promising Exploration Regions by Weighting Expected Navigation Costs continuous environments, some first-order approximations to"
    - "REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NON-DETERMINISTIC ENVIRONMENTS"
    - "Advances in Neural Information Processing Systems 8 Active Learning in Multilayer Perceptrons"
    - "An Algorithm for Active Data Collection for Learning Feasibility Study with Neural Networks."
    - "Exploration Bonuses and Dual Control"

Connected through hyperedge HE-3 (induced by source paper "Using Temporal-Difference Reinforcement Learning to Improve Decision-Theoretic Utilities for Diagnosis"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Supervised learning from incomplete data via an EM approach"
    - "On Step-Size and Bias in Temporal-Difference Learning"

Connected through hyperedge HE-4 (induced by source paper "In  Active Learning with Statistical Models"):
  with members:
    - "Supervised learning from incomplete data via an EM approach"
    - "Information-based objective functions for active data selection"
    - "Control Systems Magazine, 14, 1, pp.57-71. Robot Juggling: An Implementation of Memory-based Learning"
    - "Q2: Memory-based active learning for optimizing noisy continuous functions field expands beyond prediction and function"
    - "REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NON-DETERMINISTIC ENVIRONMENTS"
    - "An Algorithm for Active Data Collection for Learning Feasibility Study with Neural Networks."
    - "Neural Network Exploration Using Optimal Experiment Design"

Connected through hyperedge HE-5 (induced by source paper "Supervised learning from incomplete data via an EM approach"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Recurrent Neural Networks for Missing or Asynchronous Data"
    - "Using Temporal-Difference Reinforcement Learning to Improve Decision-Theoretic Utilities for Diagnosis"
    - "In  Active Learning with Statistical Models"
    - "EM Algorithms for PCA and SPCA"
    - "A Statistical Approach to Decision Tree Modeling"
    - "Learning Bayesian Networks from Incomplete Data"

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