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 "database"):
  Abstract: MIT Computational Cognitive Science Technical Report 9701 Abstract We describe variational approximation methods for efficient probabilistic reasoning, applying these methods to the problem of diagnostic inference in the QMR-DT database. The QMR-DT database is a large-scale belief network based on statistical and expert knowledge in internal medicine. The size and complexity of this network render exact probabilistic diagnosis infeasible for all but a small set of cases. This has hindered the development of the QMR- DT network as a practical diagnostic tool and has hindered researchers from exploring and critiquing the diagnostic behavior of QMR. In this paper we describe how variational approximation methods can be applied to the QMR network, resulting in fast diagnostic inference. We evaluate the accuracy of our methods on a set of standard diagnostic cases and compare to stochastic sampling methods.

Includes members:
  Member "Markov games as a framework for multi-agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Reinforcement Learning with Imitation in Heterogeneous Multi-Agent Systems")
  Member "Computing upper and lower bounds on likelihoods in intractable networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "A variational approach to Bayesian logistic regression models and their extensions")
  Member "Recursive algorithms for approximating probabilities in graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Boltzmann Machine learning using mean field theory and linear response correction")

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