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: "Bumptrees for Efficient Function, Constraint, and Classification Learning"
  Abstract: A new class of data structures called bumptrees is described. These structures are useful for efficiently implementing a number of neural network related operations. An empirical comparison with radial basis functions is presented on a robot arm mapping learning task. Applications to density estimation, classification, and constraint representation and learning are also outlined.

Connected through hyperedge HE-1 (induced by source paper "Efficient Locally Weighted Polynomial Regression Predictions"):
  with members:
    - "Value Function Based Production Scheduling"
    - "Category: Control, Navigation and Planning Preference: Oral presentation Exploiting Model Uncertainty Estimates for Safe Dynamic"
    - "Control Systems Magazine, 14, 1, pp.57-71. Robot Juggling: An Implementation of Memory-based Learning"
    - "On the Greediness of Feature Selection Algorithms"

Connected through hyperedge HE-2 (induced by source paper "Best-First Model Merging for Dynamic Learning and Recognition"):
  with members:
    - "A Practical Bayesian Framework for Backprop Networks"

Connected through hyperedge HE-3 (induced by source paper "Fast Bounded Smooth Regression with Lazy Neural Trees"):
  with members:
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"

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