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 "248 Efficient Superscalar Performance Through Boosting"):
  Abstract: The foremost goal of superscalar processor design is to increase performance through the exploitation of instruction-level parallelism (ILP). Previous studies have shown that speculative execution is required for high instruction per cycle (IPC) rates in non-numerical applications. The general trend has been toward supporting speculative execution in complicated, dynamically-scheduled processors. Performance, though, is more than just a high IPC rate; it also depends upon instruction count and cycle time. Boosting is an architectural technique that supports general speculative execution in simpler, statically-scheduled processors. Boosting labels speculative instructions with their control dependence information. This labelling eliminates control dependence constraints on instruction scheduling while still providing full dependence information to the hardware. We have incorporated boosting into a trace-based, global scheduling algorithm that exploits ILP without adversely affecting the instruction count of a program. We use this algorithm and estimates of the boosting hardware involved to evaluate how much speculative execution support is really necessary to achieve good performance. We find that a statically-scheduled superscalar processor using a minimal implementation of boosting can easily reach the performance of a much more complex dynamically-scheduled superscalar processor.

Includes members:
  Member "The Limits of Instruction Level Parallelism in SPEC95 Applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Limits of Control Flow on Parallelism")
  Member "Instructions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Limits of Control Flow on Parallelism")
  Member "Resource Spackling: A Framework for Integrating Register Allocation in Local and Global Schedulers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "GURRR: A Global Unified Resource Requirements Representation")
  Member "GURRR: A Global Unified Resource Requirements Representation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Resource Spackling: A Framework for Integrating Register Allocation in Local and Global Schedulers")

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