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: "d d Techniques for Extracting Instruction Level Parallelism on MIMD Architectures"
  Abstract: Extensive research has been done on extracting parallelism from single instruction stream processors. This paper presents some results of our investigation into ways to modify MIMD architectures to allow them to extract the instruction level parallelism achieved by current superscalar and VLIW machines. A new architecture is proposed which utilizes the advantages of a multiple instruction stream design while addressing some of the limitations that have prevented MIMD architectures from performing ILP operation. A new code scheduling mechanism is described to support this new architecture by partitioning instructions across multiple processing elements in order to exploit this level of parallelism.

Connected through hyperedge HE-1 (induced by source paper "Converting Thread-Level Parallelism to Instruction-Level Parallelism via Simultaneous Multithreading"):
  with members:
    - "Exploiting Choice: Instruction Fetch and Issue on an Implementable Simultaneous Multithreading Processor"
    - "d d MISC: A Multiple Instruction Stream Computer"

Connected through hyperedge HE-2 (induced by source paper "The Limits of Instruction Level Parallelism in SPEC95 Applications"):
  with members:
    - "THE EXPANDABLE SPLIT WINDOW PARADIGM FOR EXPLOITING FINE-GRAIN PARALLELISM"
    - "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 "d d MISC: A Multiple Instruction Stream Computer"):
  with members:
    - "Converting Thread-Level Parallelism to Instruction-Level Parallelism via Simultaneous Multithreading"
    - "d d Code Scheduling for Multiple Instruction Stream Architectures"

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