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 Formalization of Explanation-Based Macro-operator Learning"
  Abstract: In spite of the popularity of Explanation-Based Learning (EBL), its theoretical basis is not well-understood. Using a generalization of Probably Approximately Correct (PAC) learning to problem solving domains, this paper formalizes two forms of Explanation-Based Learning of macro-operators and proves the sufficient conditions for their success. These two forms of EBL, called "Macro Caching" and "Serial Parsing," respectively exhibit two distinct sources of power or "bias": the sparseness of the solution space and the decomposability of the problem-space. The analysis shows that exponential speedup can be achieved when either of these biases is suitable for a domain. Somewhat surprisingly, it also shows that computing the preconditions of the macro-operators is not necessary to obtain these speedups. The theoretical results are confirmed by experiments in the domain of Eight Puzzle. Our work suggests that the best way to address the utility problem in EBL is to implement a bias which exploits the problem-space structure of the set of domains that one is interested in learning.

Connected through hyperedge HE-1 (induced by source paper "Rationality and Intelligence"):
  with members:
    - "Approximating Optimal Policies for Partially Observable Stochastic Domains"
    - "Toward Efficient Agnostic Learning"
    - "The BATmobile: Towards a Bayesian Automated Taxi"

Connected through hyperedge HE-2 (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 from Queries and Examples with Tree-structured Bias"
    - "Learning in the Presence of Prior Knowledge: A Case Study Using Model Calibration"

Connected through hyperedge HE-3 (induced by source paper "A Theory of Unsupervised Speedup Learning"):
  with members:
    (none)

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