human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Quantifying Prior Determination Knowledge using the PAC Learning Model"):
  Abstract: Prior knowledge, or bias, regarding a concept can speed up the task of learning it. Probably Approximately Correct (PAC) learning is a mathematical model of concept learning that can be used to quantify the speed up due to different forms of bias on learning. Thus far, PAC learning has mostly been used to analyze syntactic bias, such as limiting concepts to conjunctions of boolean prepositions. This paper demonstrates that PAC learning can also be used to analyze semantic bias, such as a domain theory about the concept being learned. The key idea is to view the hypothesis space in PAC learning as that consistent with all prior knowledge, syntactic and semantic. In particular, the paper presents a PAC analysis of determinations, a type of relevance knowledge. The results of the analysis reveal crisp distinctions and relations among different determinations, and illustrate the usefulness of an analysis based on the PAC model.

Includes members:
  Member "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "A General Lower Bound on the Number of Examples Needed for Learning")
  Member "Finding new rules for incomplete theories: Explicit biases for induction with contextual information. In Proceedings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine")
  Member "Learning from Queries and Examples with Tree-structured Bias":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata")
  Member "Learning in the Presence of Prior Knowledge: A Case Study Using Model Calibration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Automated Decomposition of Model-based Learning Problems")
  Member "A Formalization of Explanation-Based Macro-operator Learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Rationality and Intelligence")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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