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: "Learning Using Group Representations (Extended Abstract)"
  Abstract: We consider the problem of learning functions over a fixed distribution. An algorithm by Kushilevitz and Mansour [7] learns any boolean function over f0; 1g n in time polynomial in the L 1 -norm of the Fourier transform of the function. We show that the KM-algorithm is a special case of a more general class of learning algorithms. This is achieved by extending their ideas using representations of finite groups. We introduce some new classes of functions which can be learned using this generalized KM algorithm.

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"
    - "Implementation Issues in the Fourier Transform Algorithm"
    - "An O(n log log n Learning Algorithm for DNF under the Uniform Distribution"

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"
    - "Implementation Issues in the Fourier Transform Algorithm"

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