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: "Using Partitioning to Speed Up Specific-to-General Rule Induction"
  Abstract: RISE (Domingos 1995; in press) is a rule induction algorithm that proceeds by gradually generalizing rules, starting with one rule per example. This has several advantages compared to the more common strategy of gradually specializing initially null rules, and has been shown to lead to significant accuracy gains over algorithms like C4.5RULES and CN2 in a large number of application domains. However, RISE's running time (like that of other rule induction algorithms) is quadratic in the number of examples, making it unsuitable for processing very large databases. This paper studies the use of partitioning to speed up RISE, and compares it with the well-known method of windowing. The use of partitioning in a specific-to-general induction setting creates synergies that would not be possible with a general-to-specific system. Partitioning often reduces running time and improves accuracy at the same time. In noisy conditions, the performance of windowing deteriorates rapidly, while that of partitioning remains stable.

Connected through hyperedge HE-1 (induced by source paper "Rule Induction and Instance-Based Learning: A Unified Approach"):
  with members:
    - "Distance Induction in First Order Logic used for classification via a k-nearest-neighbor process. Experiments on"
    - "Rule Generation and Compaction in the wwtp"
    - "Inductive CBR for Customer Support"

Connected through hyperedge HE-2 (induced by source paper "Concept Learning and the Problem of Small"):
  with members:
    - "Unifying Empirical and Explanation-Based Learning by Modeling the Utility of Learned Knowledge"
    - "Fossil: A Robust Relational Learner"
    - "The RISE System: Conquering Without Separating"
    - "Learning with Rare Cases and Small Disjuncts"
    - "The Problem with Noise and Small Disjuncts"
    - "Pessimistic and Optimistic Induction"
    - "On Learning Multiple Descriptions of a Concept"

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