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: "Within the Letter of the Law: open-textured planning"
  Abstract: Most case-based reasoning systems have used a single "best" or "most similar" case as the basis for a solution. For many problems, however, there is no single exact solution. Rather, there is a range of acceptable answers. We use cases not only as a basis for a solution, but also to indicate the boundaries within which a solution can be found. We solve problems by choosing some point within those boundaries. In this paper, I discuss this use of cases with illustrations from chiron, a system I have implemented in the domain of personal income tax planning.

Connected through hyperedge HE-1 (induced by source paper "Massively Parallel Support for Case-based Planning"):
  with members:
    - "The Case for Graph-Structured Representations"
    - "Protein Sequencing Experiment Planning Using Analogy protein sequencing experiments. Planning is interleaved with experiment execution,"

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: Case_Based