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: "The Limits of Instruction Level Parallelism in SPEC95 Applications"
  Abstract: This paper examines the limits to instruction level parallelism that can be found in programs, in particular the SPEC95 benchmark suite. It differs from earlier studies in removing non-essential true dependencies that occur as a result of the compiler employing a stack for subroutine linkage. This is a subtle limitation to parallelism that is not readily evident as it appears as a true dependency on the stack pointer. In this paper we show that its removal exposes far more parallelism than has been seen previously. We refer to this type of parallelism as "parallelism at a distance" because it requires impossibly large instruction windows for detection. We conclude with two observations: 1) that a single instruction window characteristic of superscalar machines is inadequate for detecting parallelism at a distance; and 2) in order to take advantage of this parallelism the compiler must be involved, or separate threads must be explicitly programmed.

Connected through hyperedge HE-1 (induced by source paper "248 Efficient Superscalar Performance Through Boosting"):
  with members:
    - "Instructions"
    - "Resource Spackling: A Framework for Integrating Register Allocation in Local and Global Schedulers"
    - "GURRR: A Global Unified Resource Requirements Representation"

Connected through hyperedge HE-2 (induced by source paper "Limits of Control Flow on Parallelism"):
  with members:
    - "Control Flow Prediction For Dynamic ILP Processors"
    - "Instructions"
    - "Theoretical Modeling of Superscalar Processor Performance"
    - "Space-Time Scheduling of Instruction-Level Parallelism on a Raw Machine"

Connected through hyperedge HE-3 (induced by source paper "d d Techniques for Extracting Instruction Level Parallelism on MIMD Architectures"):
  with members:
    - "Converting Thread-Level Parallelism to Instruction-Level Parallelism via Simultaneous Multithreading"
    - "d d MISC: A Multiple Instruction Stream Computer"

Connected through hyperedge HE-4 (induced by source paper "Theoretical Modeling of Superscalar Processor Performance"):
  with members:
    - "Limits of Control Flow on Parallelism"

Connected through hyperedge HE-5 (induced by source paper "A Comparison of Full and Partial Predicated Execution Support for ILP Processors"):
  with members:
    - "Dynamic Hammock Predication for Non-predicated Instruction Set Architectures"

Connected through hyperedge HE-6 (induced by source paper "d d Code Scheduling for Multiple Instruction Stream Architectures"):
  with members:
    - "d d MISC: A Multiple Instruction Stream Computer"

Connected through hyperedge HE-7 (induced by source paper "THE EXPANDABLE SPLIT WINDOW PARADIGM FOR EXPLOITING FINE-GRAIN PARALLELISM"):
  with members:
    - "Control Flow Prediction For Dynamic ILP Processors"
    - "Task Selection for a Multiscalar Processor"

Connected through hyperedge HE-8 (induced by source paper "Instructions"):
  with members:
    - "Limits of Control Flow on Parallelism"
    - "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