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: "Learning Singly-Recursive Relations from Small Datasets"
  Abstract: The inductive logic programming system LOPSTER was created to demonstrate the advantage of basing induction on logical implication rather than -subsumption. LOPSTER's sub-unification procedures allow it to induce recursive relations using a minimum number of examples, whereas inductive logic programming algorithms based on -subsumption require many more examples to solve induction tasks. However, LOPSTER's input examples must be carefully chosen; they must be along the same inverse resolution path. We hypothesize that an extension of LOPSTER can efficiently induce recursive relations without this requirement. We introduce a generalization of LOPSTER named CRUSTACEAN that has this capability and empirically evaluate its ability to induce recursive relations.

Connected through hyperedge HE-1 (induced by source paper "The Difficulties of Learning Logic Programs with Cut"):
  with members:
    - "The Challenge of Revising an Impure Theory"
    - "on Inductive Logic Programming (ILP-95) Inducing Logic Programs without Explicit Negative Examples"

Connected through hyperedge HE-2 (induced by source paper "Inverting Implication with Small Training Sets"):
  with members:
    - "Bottom-up induction of logic programs with more than one recursive clause"

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