human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Supervised learning from incomplete data via an EM approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Using Temporal-Difference Reinforcement Learning to Improve Decision-Theoretic Utilities for Diagnosis")
  Member "Using Temporal-Difference Reinforcement Learning to Improve Decision-Theoretic Utilities for Diagnosis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Supervised learning from incomplete data via an EM approach")
  Member "In  Active Learning with Statistical Models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Information-based objective functions for active data selection")
  Member "Information-based objective functions for active data selection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "A Practical Bayesian Framework for Backprop Networks")
  Member "Neural Network Exploration Using Optimal Experiment Design":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Advances in Neural Information Processing Systems 8 Active Learning in Multilayer Perceptrons")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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