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: "Efficient Locally Weighted Polynomial Regression Predictions"
  Abstract: Locally weighted polynomial regression (LWPR) is a popular instance-based algorithm for learning continuous non-linear mappings. For more than two or three inputs and for more than a few thousand dat-apoints the computational expense of predictions is daunting. We discuss drawbacks with previous approaches to dealing with this problem, and present a new algorithm based on a multiresolution search of a quickly-constructible augmented kd-tree. Without needing to rebuild the tree, we can make fast predictions with arbitrary local weighting functions, arbitrary kernel widths and arbitrary queries. The paper begins with a new, faster, algorithm for exact LWPR predictions. Next we introduce an approximation that achieves up to a two-orders-of-magnitude speedup with negligible accuracy losses. Increasing a certain approximation parameter achieves greater speedups still, but with a correspondingly larger accuracy degradation. This is nevertheless useful during operations such as the early stages of model selection and locating optima of a fitted surface. We also show how the approximations can permit real-time query-specific optimization of the kernel width. We conclude with a brief discussion of potential extensions for tractable instance-based learning on datasets that are too large to fit in a com puter's main memory.

Connected through hyperedge HE-1 (induced by source paper "On the Greediness of Feature Selection Algorithms"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms"
    - "Toward Optimal Feature Selection"

Connected through hyperedge HE-2 (induced by source paper "Control Systems Magazine, 14, 1, pp.57-71. Robot Juggling: An Implementation of Memory-based Learning"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "In  Active Learning with Statistical Models"
    - "Forward models: Supervised learning with a distal teacher"
    - "Reinforcement Learning in the Multi-Robot Domain"
    - "Locally Weighted Learning for Control"
    - "Using Local Trajectory Optimizers To Speed Up Global Optimization In Dynamic Programming"

Connected through hyperedge HE-3 (induced by source paper "Value Function Based Production Scheduling"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "A Reinforcement Learning Approach to Job-shop Scheduling"
    - "Self-Improving Factory Simulation using Continuous-time Average-Reward Reinforcement Learning"

Connected through hyperedge HE-4 (induced by source paper "Bumptrees for Efficient Function, Constraint, and Classification Learning"):
  with members:
    - "Best-First Model Merging for Dynamic Learning and Recognition"
    - "Fast Bounded Smooth Regression with Lazy Neural Trees"

Connected through hyperedge HE-5 (induced by source paper "Category: Control, Navigation and Planning Preference: Oral presentation Exploiting Model Uncertainty Estimates for Safe Dynamic"):
  with members:
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Memory Based Stochastic Optimization for Validation and Tuning of Function Approximators"
    - "Using Local Trajectory Optimizers To Speed Up Global Optimization In Dynamic Programming"

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: Case_Based