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: "Control Flow Prediction For Dynamic ILP Processors"
  Abstract: We introduce a technique to enhance the ability of dynamic ILP processors to exploit (speculatively executed) parallelism. Existing branch prediction mechanisms used to establish a dynamic window from which ILP can be extracted are limited in their abilities to: (i) create a large, accurate dynamic window, (ii) initiate a large number of instructions into this window in every cycle, and (iii) traverse multiple branches of the control flow graph per prediction. We introduce control flow prediction which uses information in the control flow graph of a program to overcome these limitations. We discuss how information present in the control flow graph can be represented using multiblocks, and conveyed to the hardware using Control Flow Tables and Control Flow Prediction Buffers. We evaluate the potential of control flow prediction on an abstract machine and on a dynamic ILP processing model. Our results indicate that control flow prediction is a powerful and effective assist to the hardware in making more informed run time decisions about program control flow.

Connected through hyperedge HE-1 (induced by source paper "Limits of Control Flow on Parallelism"):
  with members:
    - "The Limits of Instruction Level Parallelism in SPEC95 Applications"
    - "Instructions"
    - "Theoretical Modeling of Superscalar Processor Performance"
    - "Space-Time Scheduling of Instruction-Level Parallelism on a Raw Machine"

Connected through hyperedge HE-2 (induced by source paper "THE EXPANDABLE SPLIT WINDOW PARADIGM FOR EXPLOITING FINE-GRAIN PARALLELISM"):
  with members:
    - "The Limits of Instruction Level Parallelism in SPEC95 Applications"
    - "Task Selection for a Multiscalar Processor"

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