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: "A Quantum Computational Learning Algorithm"
  Abstract: An interesting classical result due to Jackson allows polynomial-time learning of the function class DNF using membership queries. Since in most practical learning situations access to a membership oracle is unrealistic, this paper explores the possibility that quantum computation might allow a learning algorithm for DNF that relies only on example queries. A natural extension of Fourier-based learning into the quantum domain is presented. The algorithm requires only an example oracle, and it runs in O( 2 n ) time, a result that appears to be classically impossible. The algorithm is unique among quantum algorithms in that it does not assume a priori knowledge of a function and does not operate on a superposition that includes all possible basis states.

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"
    - "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"
    - "Learning Using Group Representations (Extended Abstract)"

Connected through hyperedge HE-2 (induced by source paper "Boosting a weak learning algorithm by majority To be published in Information and Computation"):
  with members:
    - "General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Boosting"
    - "Sifting informative examples from a random source."
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "Efficient Distribution-free Learning of Probabilistic Concepts"
    - "Learning From a Population of Hypotheses"
    - "Game Theory, On-line Prediction and Boosting"
    - "On the Sample Complexity of Weakly 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: Theory