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: "Structured Representation of Complex Stochastic Systems"
  Abstract: This paper considers the problem of representing complex systems that evolve stochastically over time. Dynamic Bayesian networks provide a compact representation for stochastic processes. Unfortunately, they are often unwieldy since they cannot explicitly model the complex organizational structure of many real life systems: the fact that processes are typically composed of several interacting subprocesses, each of which can, in turn, be further decomposed. We propose a hierarchically structured representation language which extends both dynamic Bayesian networks and the object-oriented Bayesian network framework of [9], and show that our language allows us to describe such systems in a natural and modular way. Our language supports a natural representation for certain system characteristics that are hard to capture using more traditional frameworks. For example, it allows us to represent systems where some processes evolve at a different rate than others, or systems where the processes interact only intermittently. We provide a simple inference mechanism for our representation via translation to Bayesian networks, and suggest ways in which the inference algorithm can exploit the additional structure encoded in our representation.

Connected through hyperedge HE-1 (induced by source paper "Context-Specific Independence in Bayesian Networks"):
  with members:
    - "BUCKET ELIMINATION: A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE"
    - "Exploiting Causal Independence in Bayesian Network Inference"
    - "Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks"
    - "The Frame Problem and Bayesian Network Action Representations"
    - "A Constraint-Based Approach to Preference Elicitation and Decision Making"
    - "LEARNING BAYESIAN NETWORKS WITH LOCAL STRUCTURE"

Connected through hyperedge HE-2 (induced by source paper "Tractable Inference for Complex Stochastic Processes"):
  with members:
    - "Exploiting Tractable Substructures in Intractable Networks"
    - "Factorial Hidden Markov Models"
    - "Stochastic simulation algorithms for dynamic probabilistic networks"
    - "The BATmobile: Towards a Bayesian Automated Taxi"
    - "Probabilistic Independence Networks for Hidden Markov Probability Models"

Connected through hyperedge HE-3 (induced by source paper "Factorial Hidden Markov Models"):
  with members:
    - "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"
    - "Space-efficient inference in dynamic probabilistic networks"
    - "Speech Recognition with Dynamic Bayesian Networks"

Connected through hyperedge HE-4 (induced by source paper "Stochastic simulation algorithms for dynamic probabilistic networks"):
  with members:
    - "Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks"
    - "Dynamic Belief Networks for Discrete Monitoring"
    - "Tractable Inference for Complex Stochastic Processes"
    - "The BATmobile: Towards a Bayesian Automated Taxi"

Connected through hyperedge HE-5 (induced by source paper "Global Conditioning for Probabilistic Inference in Belief Networks"):
  with members:
    - "BUCKET ELIMINATION: A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE"
    - "Topological Parameters for time-space tradeoff"
    - "Logarithmic Time Parallel Bayesian Inference"
    - "Automated Decomposition of Model-based Learning Problems"

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