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: "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.

Connected through hyperedge HE-1 (induced by source paper "Computing upper and lower bounds on likelihoods in intractable networks"):
  with members:
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Exploiting Tractable Substructures in Intractable Networks"
    - "A variational approach to Bayesian logistic regression models and their extensions"
    - "Recursive algorithms for approximating probabilities in graphical models"
    - "Using Qualitative Relationships for Bounding Probability Distributions"

Connected through hyperedge HE-2 (induced by source paper "Markov games as a framework for multi-agent reinforcement learning"):
  with members:
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "A Competitive Approach to Game Learning"
    - "Learning Team Strategies With Multiple Policy-Sharing Agents: A Soccer Case Study"
    - "Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm"
    - "Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents"
    - "Learning Conventions in Multiagent Stochastic Domains using Likelihood Estimates"
    - "Reinforcement Learning with Imitation in Heterogeneous Multi-Agent Systems"
    - "Using Communication to Reduce Locality in Distributed Multi-Agent Learning"

Connected through hyperedge HE-3 (induced by source paper "Recursive algorithms for approximating probabilities in graphical models"):
  with members:
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Computing upper and lower bounds on likelihoods in intractable networks"
    - "Exploiting Tractable Substructures in Intractable Networks"
    - "A variational approach to Bayesian logistic regression models and their extensions"
    - "Boltzmann Machine learning using mean field theory and linear response correction"

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