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: "Dynamic Belief Networks for Discrete Monitoring"
  Abstract: We describe the development of a monitoring system which uses sensor observation data about discrete events to construct dynamically a probabilistic model of the world. This model is a Bayesian network incorporating temporal aspects, which we call a Dynamic Belief Network; it is used to reason under uncertainty about both the causes and consequences of the events being monitored. The basic dynamic construction of the network is data-driven. However the model construction process combines sensor data about events with externally provided information about agents' behaviour, and knowledge already contained within the model, to control the size and complexity of the network. This means that both the network structure within a time interval, and the amount of history and detail maintained, can vary over time. We illustrate the system with the example domain of monitoring robot vehicles and people in a restricted dynamic environment using light-beam sensor data. In addition to presenting a generic network structure for monitoring domains, we describe the use of more complex network structures which address two specific monitoring problems, sensor validation and the Data Association Problem.

Connected through hyperedge HE-1 (induced by source paper "Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Generalization in Reinforcement Learning: Safely Approximating the Value Function"
    - "Using a Case Base of Surfaces to Speed-Up Reinforcement Learning"
    - "Auto-exploratory Average Reward Reinforcement Learning"
    - "Generalized Prioritized Sweeping"

Connected through hyperedge HE-2 (induced by source paper "Fall Diagnosis using Dynamic Belief Networks"):
  with members:
    - "The BATmobile: Towards a Bayesian Automated Taxi"

Connected through hyperedge HE-3 (induced by source paper "Introduction to the Special Section on Knowledge-Based Construction of Probabilistic and Decision Models (IEEE Transactions"):
  with members:
    - "State-Space Abstraction for Anytime Evaluation of Probabilistic Networks"
    - "Abstract"
    - "Bayesian Graphical Modeling for Intelligent Tutoring Systems"
    - "The Automated Mapping of Plans for Plan Recognition"

Connected through hyperedge HE-4 (induced by source paper "State-Space Abstraction for Anytime Evaluation of Probabilistic Networks"):
  with members:
    - "Using Qualitative Relationships for Bounding Probability Distributions"
    - "Incremental Tradeoff Resolution in Qualitative Probabilistic Networks"
    - "Computational complexity reduction for BN2O networks using similarity of states"
    - "Introduction to the Special Section on Knowledge-Based Construction of Probabilistic and Decision Models (IEEE Transactions"

Connected through hyperedge HE-5 (induced by source paper "Stochastic simulation algorithms for dynamic probabilistic networks"):
  with members:
    - "Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks"
    - "Structured Representation of Complex Stochastic Systems"
    - "Tractable Inference for Complex Stochastic Processes"
    - "The BATmobile: Towards a Bayesian Automated Taxi"

Connected through hyperedge HE-6 (induced by source paper "Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks"):
  with members:
    - "Context-Specific Independence in Bayesian Networks"
    - "The Frame Problem and Bayesian Network Action Representations"
    - "LEARNING BAYESIAN NETWORKS WITH LOCAL STRUCTURE"
    - "Stochastic simulation algorithms for dynamic probabilistic networks"

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