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: "Space-efficient inference in dynamic probabilistic networks"
  Abstract: Dynamic probabilistic networks (DPNs) are a useful tool for modeling complex stochastic processes. The simplest inference task in DPNs is monitoring | that is, computing a posterior distribution for the state variables at each time step given all observations up to that time. Recursive, constant-space algorithms are well-known for monitoring in DPNs and other models. This paper is concerned with hindsight | that is, computing a posterior distribution given both past and future observations. Hindsight is an essential subtask of learning DPN models from data. Existing algorithms for hindsight in DPNs use O(SN ) space and time, where N is the total length of the observation sequence and S is the state space size for each time step. They are therefore impractical for hindsight in complex models with long observation sequences. This paper presents an O(S log N ) space, O(SN log N ) time hindsight algorithm. We demonstrates the effectiveness of the algorithm in two real-world DPN learning problems. We also discuss the possibility of an O(S)-space, O(SN )-time algorithm.

Connected through hyperedge HE-1 (induced by source paper "Factorial Hidden Markov Models"):
  with members:
    - "Structured Representation of Complex Stochastic Systems"
    - "Hidden Markov decision trees"
    - "IMPROVING THE MEAN FIELD APPROXIMATION VIA THE USE OF MIXTURE DISTRIBUTIONS"
    - "Coupled hidden Markov models for complex action recognition"
    - "Tractable Inference for Complex Stochastic Processes"
    - "MIXED MEMORY MARKOV MODELS FOR TIME SERIES ANALYSIS"
    - "Speech Recognition with Dynamic Bayesian Networks"

Connected through hyperedge HE-2 (induced by source paper "The BATmobile: Towards a Bayesian Automated Taxi"):
  with members:
    - "Rationality and Intelligence"
    - "Adaptive probabilistic networks"
    - "Stochastic simulation algorithms for dynamic probabilistic networks"
    - "Tractable Inference for Complex Stochastic Processes"
    - "Accounting for Context in Plan Recognition, with Application to Traffic Monitoring"
    - "Fall Diagnosis using Dynamic Belief Networks"

Connected through hyperedge HE-3 (induced by source paper "Probabilistic Independence Networks for Hidden Markov Probability Models"):
  with members:
    - "On Structured Variational Approximations"
    - "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View"
    - "Coupled hidden Markov models for complex action recognition"
    - "Tractable Inference for Complex Stochastic Processes"
    - "Belief Propagation and Revision in Networks with Loops"
    - "Speech Recognition with Dynamic Bayesian 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