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 Heuristic Approach to the Discovery of Macro-operators. Machine Learning, 3, 285-317. L e a"
  Abstract: The negative effect is naturally more significant in the more complex domain. The graph for the simple domain crosses the 0 line earlier than the complex domain. That means that learning starts to be useful with weight greater than 0.6 for the simple domain and 0.7 for the complex domain. As we relax the optimality requirement more s i g n i f i c a n t l y ( w i t h a W = 0.8), macro usage in the more complex domain becomes more advantageous. The purpose of the research described in this paper is to identify the parameters that effects deductive learning and to perform experiments systematically in order to understand the nature of those effects. The goal of this paper is to demonstrate the methodology of performing parametric experimental study of deductive learning. The example here include the study of two parameters: the point on the satisficing-optimizing scale that is used during the search carried out during problem solving time and during learning time. We showed that A*, which looks for optimal solutions, cannot benefit from macro learning but as the strategy comes closer to best-first (satisficing search), the utility of macros increases. We also demonstrated that deductive learners that learn offline by solving training problems are sensitive to the type of search used during the learning. We showed that in general optimizing search is best for learning. It generates macros that increase the quality solutions regardless of the search method used during problem solving. It also improves the efficiency for problem solvers that require a high level of optimality. The only drawback in using optimizing search is the increase in learning resources spent. We are aware of the fact that the results described here are not very surprising. The goal of the parametric study is not necessarily to find exciting results, but to obtain results, sometimes even previously known, in a controlled experimental environment. The work described here is only part of our research plan. We are currently in the process of extensive experimentation with all the parameters described here and also with others. We also intend to test the validity of the conclusions reached during the study by repeating some of the tests in several of the commonly known search problems. We hope that such systematic experimentation will help the research community to better understand the process of deductive learning and will serve as a demonstration of the experimental methodology that should be used in machine learning research.

Connected through hyperedge HE-1 (induced by source paper "Roles of Macro-Actions in Accelerating Reinforcement Learning"):
  with members:
    - "Planning with Closed-Loop Macro Actions"
    - "Multi-time Models for Temporally Abstract Planning"

Connected through hyperedge HE-2 (induced by source paper "The Role of Forgetting in Learning"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "Utilization Filtering a method for reducing the inherent harmfulness of deductively learned knowledge field of"):
  with members:
    - "Simple Selection of Utile Control Rules in Speedup Learning"
    - "Learning Approximate Control Rules Of High Utility"

Connected through hyperedge HE-4 (induced by source paper "Computational Learning in Humans and Machines"):
  with members:
    - "Learning Semantic Grammars with Constructive Inductive Logic Programming"
    - "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms"

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