human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "The Limits of Instruction Level Parallelism in SPEC95 Applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "248 Efficient Superscalar Performance Through Boosting")
  Member "Converting Thread-Level Parallelism to Instruction-Level Parallelism via Simultaneous Multithreading":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Exploiting Choice: Instruction Fetch and Issue on an Implementable Simultaneous Multithreading Processor")
  Member "d d MISC: A Multiple Instruction Stream Computer":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Converting Thread-Level Parallelism to Instruction-Level Parallelism via Simultaneous Multithreading")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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