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: "On the Power of Equivalence Queries"
  Abstract: In 1990, Angluin showed that no class exhibiting a combinatorial property called "approximate fingerprints" can be identified exactly using polynomially many Equivalence queries (of polynomial size). Here we show that this is a necessary condition: every class without approximate fingerprints has an identification strategy that makes a polynomial number of Equivalence queries. Furthermore, if the class is "honest" in a technical sense, the computational power required by the strategy is within the polynomial-time hierarchy, so proving non learnability is at least as hard as showing P 6= NP.

Connected through hyperedge HE-1 (induced by source paper "How Many Queries are Needed to Learn?"):
  with members:
    - "Learning Conjunctions of Horn Clauses"
    - "Learning Read-Once Formulas with Queries"

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