human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "The RISE System: Conquering Without Separating"):
  Abstract: Current rule induction systems (e.g. CN2) typically rely on a "separate and conquer" strategy, learning each rule only from still-uncovered examples. This results in a dwindling number of examples being available for learning successive rules, adversely affecting the system's accuracy. An alternative is to learn all rules simultaneously, using the entire training set for each. This approach is implemented in the Rise 1.0 system. Empirical comparison of Rise with CN2 suggests that "conquering without separating" performs similarly to its counterpart in simple domains, but achieves increasingly substantial gains in accuracy as the domain difficulty grows.

Includes members:
  Member "Concept Learning and the Problem of Small":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Unifying Empirical and Explanation-Based Learning by Modeling the Utility of Learned Knowledge")
  Member "Rule Induction with CN2: Some Recent Improvements":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Multi-class problems and discretization in ICL Extended abstract")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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