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 Statistical Approach to Solving the EBL Utility Problem"
  Abstract: Many "learning from experience" systems use information extracted from problem solving experiences to modify a performance element PE, forming a new element PE 0 that can solve these and similar problems more efficiently. However, as transformations that improve performance on one set of problems can degrade performance on other sets, the new PE 0 is not always better than the original PE; this depends on the distribution of problems. We therefore seek the performance element whose expected performance, over this distribution, is optimal. Unfortunately, the actual distribution, which is needed to determine which element is optimal, is usually not known. Moreover, the task of finding the optimal element, even knowing the distribution, is intractable for most interesting spaces of elements. This paper presents a method, palo, that side-steps these problems by using a set of samples to estimate the unknown distribution, and by using a set of transformations to hill-climb to a local optimum. This process is based on a mathematically rigorous form of utility analysis: in particular, it uses statistical techniques to determine whether the result of a proposed transformation will be better than the original system. We also present an efficient way of implementing this learning system in the context of a general class of performance elements, and include empirical evidence that this approach can work effectively. fl Much of this work was performed at the University of Toronto, where it was supported by the Institute for Robotics and Intelligent Systems and by an operating grant from the National Science and Engineering Research Council of Canada. We also gratefully acknowledge receiving many helpful comments from William Cohen, Dave Mitchell, Dale Schuurmans and the anonymous referees.

Connected through hyperedge HE-1 (induced by source paper "A Formal Framework for Speedup Learning from Problems and Solutions"):
  with members:
    - "Learning policies for partially observable environments: Scaling up"

Connected through hyperedge HE-2 (induced by source paper "Probably Approximately Optimal Derivation Strategies"):
  with members:
    - "Learning Default Concepts"
    - "Learning an Optimally Accurate Representational System"
    - "ON THE SAMPLE COMPLEXITY OF FINDING GOOD SEARCH STRATEGIES 2n trials of each undetermined experiment"

Connected through hyperedge HE-3 (induced by source paper "Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation"):
  with members:
    - "Using Errors to Create Piecewise Learnable Partitions"
    - "Memory Based Stochastic Optimization for Validation and Tuning of Function Approximators"
    - "Feature Subset Selection as Search with Probabilistic Estimates"
    - "Selecting Input Variables Using Mutual Information and Nonparametric Density Estimation"

Connected through hyperedge HE-4 (induced by source paper "ON THE SAMPLE COMPLEXITY OF FINDING GOOD SEARCH STRATEGIES 2n trials of each undetermined experiment"):
  with members:
    - "Learning Default Concepts"
    - "Learning an Optimally Accurate Representational System"
    - "Probably Approximately Optimal Derivation Strategies"
    - "Learning Hierarchical Rule Sets"

Connected through hyperedge HE-5 (induced by source paper "Learning Default Concepts"):
  with members:
    - "Learning Active Classifiers"
    - "Probably Approximately Optimal Derivation Strategies"
    - "ON THE SAMPLE COMPLEXITY OF FINDING GOOD SEARCH STRATEGIES 2n trials of each undetermined experiment"
    - "Knowing What Doesn't Matter: Exploiting Omitted Superfluous Data"

Connected through hyperedge HE-6 (induced by source paper "Simple Selection of Utile Control Rules in Speedup Learning"):
  with members:
    - "Unifying Empirical and Explanation-Based Learning by Modeling the Utility of Learned Knowledge"
    - "AN EMPIRICAL APPROACH TO SOLVING THE GENERAL UTILITY PROBLEM IN SPEEDUP LEARNING"
    - "Utilization Filtering a method for reducing the inherent harmfulness of deductively learned knowledge field of"

Connected through hyperedge HE-7 (induced by source paper "Learning an Optimally Accurate Representational System"):
  with members:
    - "Probably Approximately Optimal Derivation Strategies"
    - "ON THE SAMPLE COMPLEXITY OF FINDING GOOD SEARCH STRATEGIES 2n trials of each undetermined experiment"

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