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: "Topological Parameters for time-space tradeoff"
  Abstract: In this paper we propose a family of algorithms combining tree-clustering with conditioning that trade space for time. Such algorithms are useful for reasoning in probabilistic and deterministic networks as well as for accomplishing optimization tasks. By analyzing the problem structure it will be possible to select from a spectrum the algorithm that best meets a given time-space specifica tion.

Connected through hyperedge HE-1 (induced by source paper "BUCKET ELIMINATION: A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE"):
  with members:
    - "Context-Specific Independence in Bayesian Networks"
    - "Exploiting Causal Independence in Bayesian Network Inference"
    - "Robustness Analysis of Bayesian Networks with Global Neighborhoods"
    - "Robustness Analysis of Bayesian Networks with Finitely Generated Convex Sets of Distributions"
    - "Global Conditioning for Probabilistic Inference in Belief Networks"

Connected through hyperedge HE-2 (induced by source paper "Global Conditioning for Probabilistic Inference in Belief Networks"):
  with members:
    - "BUCKET ELIMINATION: A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE"
    - "Structured Representation of Complex Stochastic Systems"
    - "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