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: "The Management of Context-Sensitive Features: A Review of Strategies"
  Abstract: In this paper, we review five heuristic strategies for handling context-sensitive features in supervised machine learning from examples. We discuss two methods for recovering lost (implicit) contextual information. We mention some evidence that hybrid strategies can have a synergetic effect. We then show how the work of several machine learning researchers fits into this framework. While we do not claim that these strategies exhaust the possibilities, it appears that the framework includes all of the techniques that can be found in the published literature on context sensitive learning.

Connected through hyperedge HE-1 (induced by source paper "A Patient-Adaptive Neural Network ECG Patient Monitoring Algorithm"):
  with members:
    - "Recognition and Exploitation of Contextual Clues via Incremental Meta-Learning (Extended Version)"
    - "Synthesize, Optimize, Analyze, Repeat (SOAR): Application of Neural Network Tools to ECG Patient Monitoring"

Connected through hyperedge HE-2 (induced by source paper "Recognition and Exploitation of Contextual Clues via Incremental Meta-Learning (Extended Version)"):
  with members:
    - "Context-sensitive attribute estimation in regression"
    - "Induction of Selective Bayesian Classifiers"
    - "Learning One More Thing"
    - "A Patient-Adaptive Neural Network ECG Patient Monitoring Algorithm"

Connected through hyperedge HE-3 (induced by source paper "Concept Learning and Flexible Weighting"):
  with members:
    - "Machine Learning: An Annotated Bibliography for the 1995 AI Statistics Tutorial on Machine Learning (Version 1)"
    - "Case-Based Sonogram Classification"
    - "Improving Minority Class Prediction Using Case-Specific Feature Weights"

Connected through hyperedge HE-4 (induced by source paper "Context-Sensitive Feature Selection for Lazy Learners"):
  with members:
    - "ICML-96 Workshop "Learning in context-sensitive domains" Bari, Italy. Dynamically Adjusting Concepts to Accommodate Changing Contexts"
    - "Learning to Refine Case Libraries:"
    - "Refining Conversational Case Libraries"
    - "An adaptation of Relief for attribute estimation in regression"
    - "Context-sensitive attribute estimation in regression"

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