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: "Applying ILP to Diterpene Structure Elucidation from 13 C NMR Spectra"
  Abstract: We present a novel application of ILP to the problem of diterpene structure elucidation from 13 C NMR spectra. Diterpenes are organic compounds of low molecular weight that are based on a skeleton of 20 carbon atoms. They are of significant chemical and commercial interest because of their use as lead compounds in the search for new pharmaceutical effectors. The structure elucidation of diterpenes based on 13 C NMR spectra is usually done manually by human experts with specialized background knowledge on peak patterns and chemical structures. In the process, each of the 20 skeletal atoms is assigned an atom number that corresponds to its proper place in the skeleton and the diterpene is classified into one of the possible skeleton types. We address the problem of learning classification rules from a database of peak patterns for diterpenes with known structure. Recently, propositional learning was successfully applied to learn classification rules from spectra with assigned atom numbers. As the assignment of atom numbers is a difficult process in itself (and possibly indistinguishable from the classification process), we apply ILP, i.e., relational learning, to the problem of classifying spectra without assigned atom numbers.

Connected through hyperedge HE-1 (induced by source paper "An intelligent search method using Inductive Logic Programming"):
  with members:
    - "Combining FOIL and EBG to Speed-up Logic Programs"

Connected through hyperedge HE-2 (induced by source paper "Generating Declarative Language Bias for Top-Down ILP Algorithms"):
  with members:
    - "Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning"
    - "A Comparison of Pruning Methods for Relational Concept Learning"
    - "Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail"
    - "Top-down Induction of Logical Decision Trees"

Connected through hyperedge HE-3 (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-4 (induced by source paper "Inductive Constraint Logic"):
  with members:
    - "Multi-class problems and discretization in ICL Extended abstract"
    - "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-5 (induced by source paper "Lookahead and Discretization in ILP"):
  with members:
    - "Solving the Multiple-Instance Problem with Axis-Parallel Rectangles"
    - "Multi-class problems and discretization in ICL Extended abstract"
    - "Top-down Induction of Logical Decision Trees"

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