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: "Tractable Inference for Complex Stochastic Processes"
  Abstract: The monitoring and control of any dynamic system depends crucially on the ability to reason about its current status and its future trajectory. In the case of a stochastic system, these tasks typically involve the use of a belief statea probability distribution over the state of the process at a given point in time. Unfortunately, the state spaces of complex processes are very large, making an explicit representation of a belief state intractable. Even in dynamic Bayesian networks (DBNs), where the process itself can be represented compactly, the representation of the belief state is intractable. We investigate the idea of maintaining a compact approximation to the true belief state, and analyze the conditions under which the errors due to the approximations taken over the lifetime of the process do not accumulate to make our answers completely irrelevant. We show that the error in a belief state contracts exponentially as the process evolves. Thus, even with multiple approximations, the error in our process remains bounded indefinitely. We show how the additional structure of a DBN can be used to design our approximation scheme, improving its performance significantly. We demonstrate the applicability of our ideas in the context of a monitoring task, showing that orders of magnitude faster inference can be achieved with only a small degradation in accuracy.

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"
    - "MIXED MEMORY MARKOV MODELS FOR TIME SERIES ANALYSIS"
    - "Space-efficient inference in dynamic probabilistic networks"
    - "Speech Recognition with Dynamic Bayesian Networks"

Connected through hyperedge HE-2 (induced by source paper "Exploiting Tractable Substructures in Intractable Networks"):
  with members:
    - "Hidden Markov decision trees"
    - "On Structured Variational Approximations"
    - "Computing upper and lower bounds on likelihoods in intractable networks"
    - "Recursive algorithms for approximating probabilities in graphical models"
    - "IMPROVING THE MEAN FIELD APPROXIMATION VIA THE USE OF MIXTURE DISTRIBUTIONS"
    - "Learning in Boltzmann Trees"
    - "Implicit learning in 3D object recognition: The importance of temporal context"
    - "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View"

Connected through hyperedge HE-3 (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"
    - "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"
    - "Stochastic simulation algorithms for dynamic probabilistic networks"

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"
    - "Dynamic Belief Networks for Discrete Monitoring"
    - "The BATmobile: Towards a Bayesian Automated Taxi"

Connected through hyperedge HE-6 (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"
    - "Space-efficient inference in dynamic probabilistic networks"
    - "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