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: "Limits of Control Flow on Parallelism"
  Abstract: This paper discusses three techniques useful in relaxing the constraints imposed by control flow on parallelism: control dependence analysis, executing multiple flows of control simultaneously, and speculative execution. We evaluate these techniques by using trace simulations to find the limits of parallelism for machines that employ different combinations of these techniques. We have three major results. First, local regions of code have limited parallelism, and control dependence analysis is useful in extracting global parallelism from different parts of a program. Second, a superscalar processor is fundamentally limited because it cannot execute independent regions of code concurrently. Higher performance can be obtained with machines, such as multiprocessors and dataflow machines, that can simultaneously follow multiple flows of control. Finally, without speculative execution to allow instructions to execute before their control dependences are resolved, only modest amounts of parallelism can be obtained for programs with complex control flow.

Connected through hyperedge HE-1 (induced by source paper "Control Flow Prediction For Dynamic ILP Processors"):
  with members:
    - "THE EXPANDABLE SPLIT WINDOW PARADIGM FOR EXPLOITING FINE-GRAIN PARALLELISM"

Connected through hyperedge HE-2 (induced by source paper "Theoretical Modeling of Superscalar Processor Performance"):
  with members:
    - "The Limits of Instruction Level Parallelism in SPEC95 Applications"

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"
    - "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-4 (induced by source paper "Space-Time Scheduling of Instruction-Level Parallelism on a Raw Machine"):
  with members:
    (none)

Connected through hyperedge HE-5 (induced by source paper "Instructions"):
  with members:
    - "The Limits of Instruction Level Parallelism in SPEC95 Applications"
    - "248 Efficient Superscalar Performance Through Boosting"

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