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: "Resource Spackling: A Framework for Integrating Register Allocation in Local and Global Schedulers"
  Abstract: We present Resource Spackling, a framework for integrating register allocation and instruction scheduling that is based on a Measure and Reduce paradigm. The technique measures the resource requirements of a program and uses the measurements to distribute code for better resource allocation. The technique is applicable to the allocation of different types of resources. A program's resource requirements for both register and functional unit resources are first measured using a unified representation. These measurements are used to find areas where resources are either under or over utilized, called resource holes and excessive sets, respectively. Conditions are determined for increasing resource utilization in the resource holes. These conditions are applicable to both local and global code motion.

Connected through hyperedge HE-1 (induced by source paper "248 Efficient Superscalar Performance Through Boosting"):
  with members:
    - "The Limits of Instruction Level Parallelism in SPEC95 Applications"
    - "Instructions"
    - "GURRR: A Global Unified Resource Requirements Representation"

Connected through hyperedge HE-2 (induced by source paper "GURRR: A Global Unified Resource Requirements Representation"):
  with members:
    - "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