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: "An Improved Algorithm for Incremental Induction of Decision Trees"
  Abstract: Technical Report 94-07 February 7, 1994 (updated April 25, 1994) This paper will appear in Proceedings of the Eleventh International Conference on Machine Learning. Abstract This paper presents an algorithm for incremental induction of decision trees that is able to handle both numeric and symbolic variables. In order to handle numeric variables, a new tree revision operator called `slewing' is introduced. Finally, a non-incremental method is given for finding a decision tree based on a direct metric of a candidate tree.

Connected through hyperedge HE-1 (induced by source paper "Learning Classification Trees"):
  with members:
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"

Connected through hyperedge HE-2 (induced by source paper "Theory Revision in Fault Hierarchies"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm"
    - "An Optimized Theory Revision Module"
    - "The Challenge of Revising an Impure Theory"

Connected through hyperedge HE-3 (induced by source paper "Decision Tree Induction Based on Efficient Tree Restructuring"):
  with members:
    - "The Power of Decision Tables"

Connected through hyperedge HE-4 (induced by source paper "Induction of Oblique Decision Trees"):
  with members:
    - "A System for Induction of Oblique Decision Trees"
    - "PATTERN RECOGNITION VIA LINEAR PROGRAMMING THEORY AND APPLICATION TO MEDICAL DIAGNOSIS"
    - "Misclassification Minimization"
    - "Misclassification Minimization"
    - "Misclassification Minimization"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Learning When Reformulation is Appropriate for Iterative Design"
    - "Lookahead and Pathology in Decision Tree Induction"

Connected through hyperedge HE-5 (induced by source paper "Lazy Induction Triggered by CBR"):
  with members:
    - "A System for Induction of Oblique Decision Trees"
    - "Inductive Learning and Case-Based Reasoning"

Connected through hyperedge HE-6 (induced by source paper "Online Learning with Random Representations"):
  with members:
    - "Locally Weighted Learning for Control"

Connected through hyperedge HE-7 (induced by source paper "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"):
  with members:
    - "Submitted to NIPS96, Section: Applications. Preference: Oral presentation Reinforcement Learning for Dynamic Channel Allocation in"
    - "High-Performance Job-Shop Scheduling With A Time-Delay TD() Network"
    - "Active Gesture Recognition using Partially Observable Markov Decision Processes"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Generalization in Reinforcement Learning: Safely Approximating the Value Function"
    - "Using a Case Base of Surfaces to Speed-Up Reinforcement Learning"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "A Reinforcement Learning Approach to Job-shop Scheduling"

Connected through hyperedge HE-8 (induced by source paper "Integration of Case-Based Reasoning and Neural Networks Approaches for Classification"):
  with members:
    - "(1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological"
    - "Inductive Learning and Case-Based Reasoning"
    - "Massively Parallel Case-Based Reasoning with Probabilistic Similarity Metrics"
    - "GAL: Networks that grow when they learn and shrink when they forget"

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