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: "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.

Connected through hyperedge HE-1 (induced by source paper "GURRR: A Global Unified Resource Requirements Representation"):
  with members:
    - "Resource Spackling: A Framework for Integrating Register Allocation in Local and Global Schedulers"

Connected through hyperedge HE-2 (induced by source paper "Resource Spackling: A Framework for Integrating Register Allocation in Local and Global Schedulers"):
  with members:
    - "GURRR: A Global Unified Resource Requirements Representation"

Connected through hyperedge HE-3 (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 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"
    - "Theoretical Modeling of Superscalar Processor Performance"

Connected through hyperedge HE-4 (induced by source paper "Instructions"):
  with members:
    - "The Limits of Instruction Level Parallelism in SPEC95 Applications"
    - "Limits of Control Flow on Parallelism"

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