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: "The Difficulties of Learning Logic Programs with Cut"
  Abstract: As real logic programmers normally use cut (!), an effective learning procedure for logic programs should be able to deal with it. Because the cut predicate has only a procedural meaning, clauses containing cut cannot be learned using an extensional evaluation method, as is done in most learning systems. On the other hand, searching a space of possible programs (instead of a space of independent clauses) is unfeasible. An alternative solution is to generate first a candidate base program which covers the positive examples, and then make it consistent by inserting cut where appropriate. The problem of learning programs with cut has not been investigated before and this seems to be a natural and reasonable approach. We generalize this scheme and investigate the difficulties that arise. Some of the major shortcomings are actually caused, in general, by the need for intensional evaluation. As a conclusion, the analysis of this paper suggests, on precise and technical grounds, that learning cut is difficult, and current induction techniques should probably be restricted to purely declarative logic languages.

Connected through hyperedge HE-1 (induced by source paper "The Challenge of Revising an Impure Theory"):
  with members:
    - "Theory Revision in Fault Hierarchies"
    - "Theory Refinement Combining Analytical and Empirical Methods"
    - "The Complexity of Theory Revision"

Connected through hyperedge HE-2 (induced by source paper "on Inductive Logic Programming (ILP-95) Inducing Logic Programs without Explicit Negative Examples"):
  with members:
    - "Learning Semantic Grammars with Constructive Inductive Logic Programming"
    - "Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models"

Connected through hyperedge HE-3 (induced by source paper "Learning Singly-Recursive Relations from Small Datasets"):
  with members:
    - "Inverting Implication with Small Training Sets"

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