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: "A Tutorial on Learning With Bayesian Networks"
  Abstract: Technical Report MSR-TR-95-06

Connected through hyperedge HE-1 (induced by source paper "Update rules for parameter estimation in Bayesian networks"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "A Comparison of New and Old Algorithms for A Mixture Estimation Problem"
    - "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide"
    - "On-Line Portfolio Selection Using Multiplicative Updates"

Connected through hyperedge HE-2 (induced by source paper "Automated Decomposition of Model-based Learning Problems"):
  with members:
    - "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide"
    - "Global Conditioning for Probabilistic Inference in Belief Networks"
    - "on Qualitative Reasoning about Physical Systems  Deriving Monotonic Function Envelopes from Observations"
    - "Learning in the Presence of Prior Knowledge: A Case Study Using Model Calibration"
    - "Learning in the Presence of Prior Knowledge: A Case Study Using Model Calibration"
    - "A Divide-and-Conquer Approach to Learning from Prior Knowledge"

Connected through hyperedge HE-3 (induced by source paper "On the Sample Complexity of Learning Bayesian Networks"):
  with members:
    - "LEARNING BAYESIAN NETWORKS WITH LOCAL STRUCTURE"

Connected through hyperedge HE-4 (induced by source paper "Bayesian Finite Mixtures for Nonlinear Modeling of Educational data"):
  with members:
    - "Comparing Bayesian Model Class Selection Criteria by Discrete Finite Mixtures"
    - "EXPERIMENTING WITH THE CHEESEMAN-STUTZ EVIDENCE APPROXIMATION FOR PREDICTIVE MODELING AND DATA MINING"

Connected through hyperedge HE-5 (induced by source paper "Learning Belief Networks in the Presence of Missing Values and Hidden Variables"):
  with members:
    - "Sequential Update of Bayesian Network Structure"

Connected through hyperedge HE-6 (induced by source paper "Learning Conventions in Multiagent Stochastic Domains using Likelihood Estimates"):
  with members:
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "Markov games as a framework for multi-agent reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "Generalized Prioritized Sweeping"):
  with members:
    - "Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "Sequential Update of Bayesian Network Structure"
    - "Tight Performance Bounds on Greedy Policies Based on Imperfect Value Functions"

Connected through hyperedge HE-8 (induced by source paper "Discovering Structure in Continuous Variables Using Bayesian Networks"):
  with members:
    - "Continuous sigmoidal belief networks trained using slice sampling"
    - "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide"

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