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: "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.

Connected through hyperedge HE-1 (induced by source paper "Learning from Queries and Examples with Tree-structured Bias"):
  with members:
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"

Connected through hyperedge HE-2 (induced by source paper "A Formalization of Explanation-Based Macro-operator Learning"):
  with members:
    - "Rationality and Intelligence"
    - "A Theory of Unsupervised Speedup Learning"

Connected through hyperedge HE-3 (induced by source paper "Learning in the Presence of Prior Knowledge: A Case Study Using Model Calibration"):
  with members:
    - "Automated Decomposition of Model-based Learning Problems"

Connected through hyperedge HE-4 (induced by source paper "Finding new rules for incomplete theories: Explicit biases for induction with contextual information. In Proceedings"):
  with members:
    - "Theory Refinement Combining Analytical and Empirical Methods"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Correcting Imperfect Domain Theories: A Knowledge-Level Analysis"

Connected through hyperedge HE-5 (induced by source paper "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"):
  with members:
    - "Boosting a weak learning algorithm by majority To be published in Information and Computation"
    - "Learning DFA from Simple Examples"
    - "A General Lower Bound on the Number of Examples Needed for Learning"
    - "Sequential PAC Learning"
    - "PAC-Learning PROLOG clauses with or without errors"
    - "Toward Efficient Agnostic Learning"
    - "Efficient Distribution-free Learning of Probabilistic Concepts"
    - "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