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: "Constructing Intermediate Concepts by Decomposition of Real Functions"
  Abstract: In learning from examples it is often useful to expand an attribute-vector representation by intermediate concepts. The usual advantage of such structuring of the learning problem is that it makes the learning easier and improves the comprehensibility of induced descriptions. In this paper, we develop a technique for discovering useful intermediate concepts when both the class and the attributes are real-valued. The technique is based on a decomposition method originally developed for the design of switching circuits and recently extended to handle incompletely specified multi-valued functions. It was also applied to machine learning tasks. In this paper, we introduce modifications, needed to decompose real functions and to present them in symbolic form. The method is evaluated on a number of test functions. The results show that the method correctly decomposes fairly complex functions. The decomposition hierarchy does not depend on a given repertoir of basic functions (background knowledge).

Connected through hyperedge HE-1 (induced by source paper "A dataset decomposition approach to data mining and machine discovery"):
  with members:
    - "Pattern Theoretic Feature Extraction and Constructive Induction"
    - "Machine Learning by Function Decomposition"

Connected through hyperedge HE-2 (induced by source paper "Machine Learning by Function Decomposition"):
  with members:
    - "A dataset decomposition approach to data mining and machine discovery"
    - "Pattern Theoretic Feature Extraction and Constructive Induction"

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