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: "A Constraint-Based Approach to Preference Elicitation and Decision Making"
  Abstract: We investigate the solution of constraint-based configuration problems in which the preference function over outcomes is unknown or incompletely specified. The aim is to configure a system, such as a personal computer, so that it will be optimal for a given user. The goal of this project is to develop algorithms that generate the most preferred feasible configuration by posing preference queries to the user. In order to minimize the number and the complexity of preference queries posed to the user, the algorithm reasons about the user's preferences while taking into account constraints over the set of feasible configurations. We assume that the user can structure their preferences in a particular way that, while natural in many settings, can be exploited during the optimization process. We also address in a preliminary fashion the trade-offs between computational effort in the solution of a problem and the degree of interaction with the user.

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"
    - "Structured Representation of Complex Stochastic Systems"
    - "The Frame Problem and Bayesian Network Action Representations"
    - "LEARNING BAYESIAN NETWORKS WITH LOCAL STRUCTURE"

Connected through hyperedge HE-2 (induced by source paper "Utility Elicitation as a Classification Problem"):
  with members:
    (none)

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