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: "Stochastic simulation algorithms for dynamic probabilistic networks"
  Abstract: Stochastic simulation algorithms such as likelihood weighting often give fast, accurate approximations to posterior probabilities in probabilistic networks, and are the methods of choice for very large networks. Unfortunately, the special characteristics of dynamic probabilistic networks (DPNs), which are used to represent stochastic temporal processes, mean that standard simulation algorithms perform very poorly. In essence, the simulation trials diverge further and further from reality as the process is observed over time. In this paper, we present simulation algorithms that use the evidence observed at each time step to push the set of trials back towards reality. The first algorithm, "evidence reversal" (ER) restructures each time slice of the DPN so that the evidence nodes for the slice become ancestors of the state variables. The second algorithm, called "survival of the fittest" sampling (SOF), "repopulates" the set of trials at each time step using a stochastic reproduction rate weighted by the likelihood of the evidence according to each trial. We compare the performance of each algorithm with likelihood weighting on the original network, and also investigate the benefits of combining the ER and SOF methods. The ER/SOF combination appears to maintain bounded error independent of the number of time steps in the simulation.

Connected through hyperedge HE-1 (induced by source paper "Tractable Inference for Complex Stochastic Processes"):
  with members:
    - "Structured Representation of Complex Stochastic Systems"
    - "Exploiting Tractable Substructures in Intractable Networks"
    - "Factorial Hidden Markov Models"
    - "The BATmobile: Towards a Bayesian Automated Taxi"
    - "Probabilistic Independence Networks for Hidden Markov Probability Models"

Connected through hyperedge HE-2 (induced by source paper "Dynamic Belief Networks for Discrete Monitoring"):
  with members:
    - "Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function"
    - "Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks"
    - "State-Space Abstraction for Anytime Evaluation of Probabilistic Networks"
    - "Introduction to the Special Section on Knowledge-Based Construction of Probabilistic and Decision Models (IEEE Transactions"
    - "Fall Diagnosis using Dynamic Belief Networks"

Connected through hyperedge HE-3 (induced by source paper "The BATmobile: Towards a Bayesian Automated Taxi"):
  with members:
    - "Rationality and Intelligence"
    - "Adaptive probabilistic networks"
    - "Tractable Inference for Complex Stochastic Processes"
    - "Space-efficient inference in dynamic probabilistic networks"
    - "Accounting for Context in Plan Recognition, with Application to Traffic Monitoring"
    - "Fall Diagnosis using Dynamic Belief Networks"

Connected through hyperedge HE-4 (induced by source paper "Structured Representation of Complex Stochastic Systems"):
  with members:
    - "Context-Specific Independence in Bayesian Networks"
    - "Global Conditioning for Probabilistic Inference in Belief Networks"
    - "Factorial Hidden Markov Models"
    - "Tractable Inference for Complex Stochastic Processes"

Connected through hyperedge HE-5 (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"
    - "Dynamic Belief Networks for Discrete Monitoring"

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