human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Automated model selection"):
  Abstract: Many algorithms have parameters that should be set by the user. For most machine learning algorithms parameter setting is a non-trivial task that influence knowledge model returned by the algorithm. Parameter values are usually set approximately according to some characteristics of the target problem, obtained in different ways. The usual way is to use background knowledge about the target problem (if any) and perform some testing experiments. The paper presents an approach to automated model selection based on local optimization that uses an empirical evaluation of the constructed concept description to guide the search. The approach was tested by using the inductive concept learning system Magnus

Includes members:
  Member "Irrelevant Features and the Subset Selection Problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "NP-Completeness of Searches for Smallest Possible Feature Sets a subset of the set of all")
  Member "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "On the Greediness of Feature Selection Algorithms")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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