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 from Queries and Examples with Tree-structured Bias"
  Abstract: Incorporating declarative bias or prior knowledge into learning is an active research topic in machine learning. Tree-structured bias specifies the prior knowledge as a tree of "relevance" relationships between attributes. This paper presents a learning algorithm that implements tree-structured bias, i.e., learns any target function probably approximately correctly from random examples and membership queries if it obeys a given tree-structured bias. The theoretical predictions of the paper are em pirically validated.

Connected through hyperedge HE-1 (induced by source paper "Quantifying Prior Determination Knowledge using the PAC Learning Model"):
  with members:
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "Finding new rules for incomplete theories: Explicit biases for induction with contextual information. In Proceedings"
    - "Learning in the Presence of Prior Knowledge: A Case Study Using Model Calibration"
    - "A Formalization of Explanation-Based Macro-operator Learning"

Connected through hyperedge HE-2 (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