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: "Implementation Issues in the Fourier Transform Algorithm"
  Abstract: The Fourier transform of boolean functions has come to play an important role in proving many important learnability results. We aim to demonstrate that the Fourier transform techniques are also a useful and practical algorithm in addition to being a powerful theoretical tool. We describe the more prominent changes we have introduced to the algorithm, ones that were crucial and without which the performance of the algorithm would severely deteriorate. One of the benefits we present is the confidence level for each prediction which measures the likelihood the prediction is correct.

Connected through hyperedge HE-1 (induced by source paper "Weakly Learning DNF and Characterizing Statistical Query Learning Using Fourier Analysis"):
  with members:
    - "On Learning Visual Concepts and DNF Formulae"
    - "Toward Efficient Agnostic Learning"
    - "A Quantum Computational Learning Algorithm"
    - "Learning Conjunctions of Horn Clauses"
    - "On Learning Read-k-Satisfy-j DNF"
    - "An O(n log log n Learning Algorithm for DNF under the Uniform Distribution"
    - "Learning Using Group Representations (Extended Abstract)"

Connected through hyperedge HE-2 (induced by source paper "An O(n log log n Learning Algorithm for DNF under the Uniform Distribution"):
  with members:
    - "Weakly Learning DNF and Characterizing Statistical Query Learning Using Fourier Analysis"
    - "Learning Using Group Representations (Extended Abstract)"

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: Theory