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: "On Learning Multiple Descriptions of a Concept"
  Abstract: In sparse data environments, greater classification accuracy can be achieved by learning several concept descriptions of the data and combining their classifications. Stochastic search is a general tool which can be used to generate many good concept descriptions (rule sets) for each class in the data. Bayesian probability theory offers an optimal strategy for combining classifications of the individual concept descriptions, and here we use an approximation of that theory. This strategy is most useful when additional data is difficult to obtain and every increase in classification accuracy is important. The primary result of this paper is that multiple concept descriptions are particularly helpful in "flat" hypothesis spaces in which there are many equally good ways to grow a rule, each having similar gain. Another result is experimental evidence that learning multiple rule sets yields more accurate classifications than learning multiple rules for some domains. To demonstrate these behaviors, we learn multiple concept descriptions by adapting HYDRA, a noise-tolerant relational learning algorithm.

Connected through hyperedge HE-1 (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"
    - "Using Partitioning to Speed Up Specific-to-General Rule Induction"

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: Rule_Learning