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: "A Reduced Multipipeline Machine Description that Preserves Scheduling Constraints"
  Abstract: High performance compilers increasingly rely on accurate modeling of the machine resources to efficiently exploit the instruction level parallelism of an application. In this paper, we propose a reduced machine description that results in faster detection of resource contentions while preserving the scheduling constraints present in the original machine description. The proposed approach reduces a machine description in an automated, error-free, and efficient fashion. Moreover, it fully supports schedulers that backtrack and process operations in arbitrary order. Reduced descriptions for the DEC Alpha 21064, MIPS R3000/R3010, and Cydra 5 result in 4 to 7 times faster detection of resource contentions and require 22 to 90% of the memory storage used by the original machine descriptions.

Connected through hyperedge HE-1 (induced by source paper "Minimum Register Requirements for a Modulo Schedule"):
  with members:
    - "Abstract"
    - "Scheduling and Mapping: Software Pipelining in the Presence of Structural Hazards proposed formulation and a"
    - "Minimizing Register Requirements under Resource-Constrained Rate-Optimal Software Pipelining"
    - "Improving Software Pipelining With Unroll-and-Jam"
    - "Stage Scheduling: A Technique to Reduce the Register Requirements of a Modulo Schedule"

Connected through hyperedge HE-2 (induced by source paper "Stage Scheduling: A Technique to Reduce the Register Requirements of a Modulo Schedule"):
  with members:
    - "Minimum Register Requirements for a Modulo Schedule"
    - "Minimizing Register Requirements under Resource-Constrained Rate-Optimal Software Pipelining"
    - "Improving Software Pipelining With Unroll-and-Jam"

Connected through hyperedge HE-3 (induced by source paper "Efficient Instruction Scheduling Using Finite State Automata"):
  with members:
    (none)

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