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: "The Case for Graph-Structured Representations"
  Abstract: Case-based reasoning involves reasoning from cases: specific pieces of experience, the reasoner's or another's, that can be used to solve problems. We use the term "graph-structured" for representations that (1) are capable of expressing the relations between any two objects in a case, (2) allow the set of relations used to vary from case to case, and (3) allow the set of possible relations to be expanded as necessary to describe new cases. Such representations can be implemented as, for example, semantic networks or lists of concrete propositions in some logic. We believe that graph-structured representations offer significant advantages, and thus we are investigating ways to implement such representations efficiently. We make a "case-based argument" using examples from two systems, chiron and caper, to show how a graph-structured representation supports two different kinds of case-based planning in two different domains. We discuss the costs associated with graph-structured representations and describe an approach to reducing those costs, imple mented in caper.

Connected through hyperedge HE-1 (induced by source paper "Massively Parallel Support for Case-based Planning"):
  with members:
    - "Within the Letter of the Law: open-textured planning"
    - "Protein Sequencing Experiment Planning Using Analogy protein sequencing experiments. Planning is interleaved with experiment execution,"

Connected through hyperedge HE-2 (induced by source paper "The case for cases: a call for purity in case-based reasoning inherently more difficult than"):
  with members:
    - "A Formal Analysis of Case Base Retrieval"

Connected through hyperedge HE-3 (induced by source paper "The Structure-Mapping Engine: Algorithm and Examples"):
  with members:
    - "Utilizing Prior Concepts for Learning"
    - "MAC/FAC: A Model of Similarity-based Retrieval"
    - "The Role of Generic Models in Conceptual Change"
    - "Adapting Abstract Knowledge"
    - "Role of Stories 1"
    - "Is analogical problem solving always analogical? The case for imitation. Second draft Is analogical problem"
    - "Functional Programming by Analogy"
    - "Modeling Analogical Problem Solving in a Production System Architecture"

Connected through hyperedge HE-4 (induced by source paper "Massively Parallel Matching of Knowledge Structures"):
  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: Case_Based