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: "Machine Learning Methods for International Conflict Databases: A Case Study in Predicting Mediation Outcome"
  Abstract: This paper tries to identify rules and factors that are predictive for the outcome of international conflict management attempts. We use C4.5, an advanced Machine Learning algorithm, for generating decision trees and prediction rules from cases in the CONFMAN database. The results show that simple patterns and rules are often not only more understandable, but also more reliable than complex rules. Simple decision trees are able to improve the chances of correctly predicting the outcome of a conflict management attempt. This suggests that mediation is more repetitive than conflicts per se, where such results have not been achieved so far.

Connected through hyperedge HE-1 (induced by source paper "Knowledge Discovery in International Conflict Databases"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "The Possible Contribution of AI to the Avoidance of Crises and Wars: Using CBR Methods"

Connected through hyperedge HE-2 (induced by source paper "Structural Regression Trees"):
  with members:
    - "Covering vs. Divide-and-Conquer for Top-Down Induction of Logic Programs"
    - "Rule-based Machine Learning Methods for Functional Prediction"
    - "Employing Linear Regression in Regression Tree Leaves"

Connected through hyperedge HE-3 (induced by source paper "Automatic Parameter Selection by Minimizing Estimated Error"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Feature Subset Selection as Search with Probabilistic Estimates"
    - "The Power of Decision Tables"
    - "Learning Symbolic Rules Using Artificial Neural Networks"
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"

Connected through hyperedge HE-4 (induced by source paper "Beyond Correlation: Bringing Artificial Intelligence to Events Data"):
  with members:
    - "A Common LISP Hypermedia Server"

Connected through hyperedge HE-5 (induced by source paper "Irrelevant Features and the Subset Selection Problem"):
  with members:
    - "Theory Revision in Fault Hierarchies"
    - "An Optimized Theory Revision Module"
    - "Expectation-Based Selective Attention for Visual Monitoring and Control of a Robot Vehicle"
    - "Feature Subset Selection as Search with Probabilistic Estimates"
    - "NP-Completeness of Searches for Smallest Possible Feature Sets a subset of the set of all"
    - "Cost-sensitive feature reduction applied to a hybrid genetic algorithm"
    - "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms"
    - "The Effective Size of a Neural Network: A Principal Component Approach"

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