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: "Introduction to the Special Section on Knowledge-Based Construction of Probabilistic and Decision Models (IEEE Transactions"
  Abstract: Modeling techniques developed recently in the AI and uncertain reasoning communities permit significantly more flexible specifications of probabilistic knowledge. Specifically, graphical decision-modeling formalisms|belief networks, influence diagrams, and their variants|provide compact representation of probabilistic relationships, and support inference algorithms that automatically exploit the dependence structure in such models [1, 3, 4]. These advances have brought on a resurgence of interest in computational decision systems based on normative theories of belief and preference. However, graphical decision-modeling languages are still quite limited for purposes of knowledge representation because, while they can describe the relationships among particular event instances, they cannot capture general knowledge about probabilistic relationships across classes of events. The inability to capture general knowledge is a serious impediment for those AI tasks in which the relevant factors of a decision problem cannot be enumerated in advance. A graphical decision model encodes a particular set of probabilistic dependencies, a predefined set of decision alternatives, and a specific mathematical form for a utility function. Given a properly specified model, there exist relatively efficient algorithms for calculating posterior probabilities and optimal decision policies. A range of similar cases may be handled by parametric variations of the original model. However, if the structure of dependencies, the set of available alternatives, or the form of utility function changes from situation to situation, then a fixed network representation is no longer adequate. An ideal computational decision system would possess general, broad knowledge of a domain, but would have the ability to reason about the particular circumstances of any given decision problem within the domain. One obvious approach|which we call call knowledge-based model construction (KBMC)|is to generate a decision model dynamically at run-time, based on the problem description and information received thus far. Model construction consists of selection, instantiation, and assembly of causal and associational relationships from a broad knowledge base of general relationships among domain concepts. For example, suppose we wish to develop a system to recommend appropriate actions for maintaining a computer network. The natural graphical decision model would include chance

Connected through hyperedge HE-1 (induced by source paper "The Automated Mapping of Plans for Plan Recognition"):
  with members:
    - "Accounting for Context in Plan Recognition, with Application to Traffic Monitoring"

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"
    - "Stochastic simulation algorithms for dynamic probabilistic networks"
    - "Fall Diagnosis using Dynamic Belief Networks"

Connected through hyperedge HE-3 (induced by source paper "Bayesian Graphical Modeling for Intelligent Tutoring Systems"):
  with members:
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"
    - "Bayesian Graphical Models for Discrete Data"

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"
    - "Dynamic Belief Networks for Discrete Monitoring"

Connected through hyperedge HE-5 (induced by source paper "Abstract"):
  with members:
    - "Representation Requirements for Supporting Decision Model Formulation"

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