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: "GURRR: A Global Unified Resource Requirements Representation"
  Abstract: When compiling for instruction level parallelism (ILP), the integration of the optimization phases can lead to an improvement in the quality of code generated. However, since several different representations of a program are used in the various phases, only a partial integration has been achieved to date. We present a program representation that combines resource requirements and availability information with control and data dependence information. The representation enables the integration of several optimizing phases, including transformations, register allocation, and instruction scheduling. The basis of this integration is the simultaneous allocation of different types of resources. We define the representation and show how it is constructed. We then formulate several optimization phases to use the representation to achieve better integration.

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"
    - "Resource Spackling: A Framework for Integrating Register Allocation in Local and Global Schedulers"

Connected through hyperedge HE-2 (induced by source paper "Resource Spackling: A Framework for Integrating Register Allocation in Local and Global Schedulers"):
  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