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: "Protein Sequencing Experiment Planning Using Analogy protein sequencing experiments. Planning is interleaved with experiment execution,"
  Abstract: Experiment design and execution is a central activity in the natural sciences. The SeqER system provides a general architecture for the integration of automated planning techniques with a variety of domain knowledge in order to plan scientific experiments. These planning techniques include rule-based methods and, especially, the use of derivational analogy. Derivational analogy allows planning experience, captured as cases, to be reused. Analogy also allows the system to function in the absence of strong domain knowledge. Cases are efficiently and flexibly retrieved from a large casebase using massively parallel methods.

Connected through hyperedge HE-1 (induced by source paper "Massively Parallel Support for Case-based Planning"):
  with members:
    - "The Case for Graph-Structured Representations"
    - "Within the Letter of the Law: open-textured planning"

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