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: "Classifiers: A Theoretical and Empirical Study"
  Abstract: This paper describes how a competitive tree learning algorithm can be derived from first principles. The algorithm approximates the Bayesian decision theoretic solution to the learning task. Comparative experiments with the algorithm and the several mature AI and statistical families of tree learning algorithms currently in use show the derived Bayesian algorithm is consistently as good or better, although sometimes at computational cost. Using the same strategy, we can design algorithms for many other supervised and model learning tasks given just a probabilistic representation for the kind of knowledge to be learned. As an illustration, a second learning algorithm is derived for learning Bayesian networks from data. Implications to incremental learning and the use of multiple models are also discussed.

Connected through hyperedge HE-1 (induced by source paper "Stochastically Guided Disjunctive Version Space Learning"):
  with members:
    - "Rule Induction with CN2: Some Recent Improvements"
    - "Error Reduction through Learning Multiple Descriptions"
    - "Learning Decision Lists Using Homogeneous Rules"

Connected through hyperedge HE-2 (induced by source paper "Is Consistency Harmful?"):
  with members:
    - "Using Genetic Algorithms for Supervised Concept Learning"

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: Probabilistic_Methods