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 EXPANDABLE SPLIT WINDOW PARADIGM FOR EXPLOITING FINE-GRAIN PARALLELISM"
  Abstract: We propose a new processing paradigm, called the Expandable Split Window (ESW) paradigm, for exploiting fine-grain parallelism. This paradigm considers a window of instructions (possibly having dependencies) as a single unit, and exploits fine-grain parallelism by overlapping the execution of multiple windows. The basic idea is to connect multiple sequential processors, in a decoupled and decentralized manner, to achieve overall multiple issue. This processing paradigm shares a number of properties of the restricted dataflow machines, but was derived from the sequential von Neumann architecture. We also present an implementation of the Expandable Split Window execution model, and preliminary performance results.

Connected through hyperedge HE-1 (induced by source paper "Control Flow Prediction For Dynamic ILP Processors"):
  with members:
    - "Limits of Control Flow on Parallelism"

Connected through hyperedge HE-2 (induced by source paper "The Limits of Instruction Level Parallelism in SPEC95 Applications"):
  with members:
    - "d d Techniques for Extracting Instruction Level Parallelism on MIMD Architectures"
    - "d d Code Scheduling for Multiple Instruction Stream Architectures"
    - "Limits of Control Flow on Parallelism"
    - "A Comparison of Full and Partial Predicated Execution Support for ILP Processors"
    - "Instructions"
    - "248 Efficient Superscalar Performance Through Boosting"
    - "Theoretical Modeling of Superscalar Processor Performance"

Connected through hyperedge HE-3 (induced by source paper "Task Selection for a Multiscalar Processor"):
  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: Rule_Learning