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: "Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation"
  Abstract: Selecting a good model of a set of input points by cross validation is a computationally intensive process, especially if the number of possible models or the number of training points is high. Techniques such as gradient descent are helpful in searching through the space of models, but problems such as local minima, and more importantly, lack of a distance metric between various models reduce the applicability of these search methods. Hoeffding Races is a technique for finding a good model for the data by quickly discarding bad models, and concentrating the computational effort at differentiating between the better ones. This paper focuses on the special case of leave-one-out cross validation applied to memory-based learning algorithms, but we also argue that it is applicable to any class of model selection problems.

Connected through hyperedge HE-1 (induced by source paper "Memory Based Stochastic Optimization for Validation and Tuning of Function Approximators"):
  with members:
    - "Category: Control, Navigation and Planning Preference: Oral presentation Exploiting Model Uncertainty Estimates for Safe Dynamic"
    - "Q2: Memory-based active learning for optimizing noisy continuous functions field expands beyond prediction and function"

Connected through hyperedge HE-2 (induced by source paper "Selecting Input Variables Using Mutual Information and Nonparametric Density Estimation"):
  with members:
    - "A Practical Bayesian Framework for Backprop Networks"
    - "The Observer-Observation Dilemma in Neuro-Forecasting: Reliable Models From Unreliable Data Through CLEARNING"

Connected through hyperedge HE-3 (induced by source paper "A Statistical Approach to Solving the EBL Utility Problem"):
  with members:
    - "A Formal Framework for Speedup Learning from Problems and Solutions"
    - "Simple Selection of Utile Control Rules in Speedup Learning"
    - "Learning Default Concepts"
    - "Learning an Optimally Accurate Representational System"
    - "Probably Approximately Optimal Derivation Strategies"
    - "ON THE SAMPLE COMPLEXITY OF FINDING GOOD SEARCH STRATEGIES 2n trials of each undetermined experiment"

Connected through hyperedge HE-4 (induced by source paper "Using Errors to Create Piecewise Learnable Partitions"):
  with members:
    (none)

Connected through hyperedge HE-5 (induced by source paper "Feature Subset Selection as Search with Probabilistic Estimates"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Estimating Attributes: Analysis and Extensions of RELIEF"
    - "Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology"
    - "Automatic Parameter Selection by Minimizing Estimated Error"

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: Theory