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: "Profile-Driven Instruction Level Parallel Scheduling with Application to Super Blocks"
  Abstract: Code scheduling to exploit instruction level parallelism (ILP) is a critical problem in compiler optimization research, in light of the increased use of long-instruction-word machines. Unfortunately, optimum scheduling is com-putationally intractable, and one must resort to carefully crafted heuristics in practice. If the scope of application of a scheduling heuristic is limited to basic blocks, considerable performance loss may be incurred at block boundaries. To overcome this obstacle, basic blocks can be coalesced across branches to form larger regions such as super blocks. In the literature, these regions are typically scheduled using algorithms that are either oblivious to profile information (under the assumption that the process of forming the region has fully utilized the profile information), or use the profile information as an addendum to classical scheduling techniques. We believe that even for the simple case of linear code regions such as super blocks, additional performance improvement can be gained by utilizing the profile information in scheduling as well. We propose a general paradigm for converting any profile-insensitive list sched-uler to a profile-sensitive scheduler. Our technique is developed via a theoretical analysis of a simplified abstract model of the general problem of profile-driven scheduling over any acyclic code region, yielding a scoring measure for ranking branch instructions. The ranking digests the profile information and has the useful property that scheduling with respect to rank is provably good for minimizing the expected completion time of the region, within the limits of the abstraction. While the ranking scheme is computation-ally intractable in the most general case, it is practicable for super blocks and suggests the heuristic that we present in this paper for profile-driven scheduling of super blocks. Experiments show that our heuristic offers substantial performance improvement over prior methods on a range of integer benchmarks and several machine models.

Connected through hyperedge HE-1 (induced by source paper "Speculative Hedge: Regulating Compile-Time Speculation Against Profile Variations code performance in the presence of execution"):
  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