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: "Massively Parallel Matching of Knowledge Structures"
  Abstract: As knowledge bases used for AI systems increase in size, access to relevant information is the dominant factor in the cost of inference. This is especially true for analogical (or case-based) reasoning, in which the ability of the system to perform inference is dependent on efficient and flexible access to a large base of exemplars (cases) judged likely to be relevant to solving a problem at hand. In this chapter we discuss a novel algorithm for efficient associative matching of relational structures in large semantic networks. The structure matching algorithm uses massively parallel hardware to search memory for knowledge structures matching a given probe structure. The algorithm is built on top of PARKA, a massively parallel knowledge representation system which runs on the Connection Machine. We are currently exploring the utility of this algorithm in CaPER, a case-based planning system.

Connected through hyperedge HE-1 (induced by source paper "The Case for Graph-Structured Representations"):
  with members:
    - "The case for cases: a call for purity in case-based reasoning inherently more difficult than"
    - "The Structure-Mapping Engine: Algorithm and Examples"
    - "Massively Parallel Support for Case-based 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