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: "Multi-class problems and discretization in ICL Extended abstract"
  Abstract: Handling multi-class problems and real numbers is important in practical applications of machine learning to KDD problems. While attribute-value learners address these problems as a rule, very few ILP systems do so. The few ILP systems that handle real numbers mostly do so by trying out all real values that are applicable, thus running into efficiency or overfitting problems. This paper discusses some recent extensions of ICL that address these problems. ICL, which stands for Inductive Constraint Logic, is an ILP system that learns first order logic formulae from positive and negative examples. The main charateristic of ICL is its view on examples. These are seen as interpretations which are true or false for the clausal target theory (in CNF). We first argue that ICL can be used for learning a theory in a disjunctive normal form (DNF). With this in mind, a possible solution for handling more than two classes is given (based on some ideas from CN2). Finally, we show how to tackle problems with continuous values by adapting discretization techniques from attribute value learners.

Connected through hyperedge HE-1 (induced by source paper "Rule Induction with CN2: Some Recent Improvements"):
  with members:
    - "Stochastically Guided Disjunctive Version Space Learning"
    - "Using Qualitative Models to Guide Inductive Learning"
    - "Case-Based Sonogram Classification"
    - "Relating Relational Learning Algorithms"
    - "Generalizing from Case Studies: A Case Study"
    - "Incremental Reduced Error Pruning"
    - "What do Constructive Learners Really Learn?"
    - "Unsupervised Constructive Learning"

Connected through hyperedge HE-2 (induced by source paper "Inductive Constraint Logic"):
  with members:
    - "Applying ILP to Diterpene Structure Elucidation from 13 C NMR Spectra"
    - "Applications of a logical discovery engine"
    - "Learning with Abduction"
    - "Inductive Constraint Logic and the Mutagenesis Problem"
    - "Relational Knowledge Discovery in Databases"
    - "The ILP description learning problem: Towards a general model-level definition of data mining in ILP"

Connected through hyperedge HE-3 (induced by source paper "Lookahead and Discretization in ILP"):
  with members:
    - "Solving the Multiple-Instance Problem with Axis-Parallel Rectangles"
    - "Applying ILP to Diterpene Structure Elucidation from 13 C NMR Spectra"
    - "Top-down Induction of Logical Decision Trees"

Connected through hyperedge HE-4 (induced by source paper "Inductive Constraint Logic and the Mutagenesis Problem"):
  with members:
    - "Applications of a logical discovery engine"
    - "Inductive Constraint Logic"

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