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: "Speculative Hedge: Regulating Compile-Time Speculation Against Profile Variations code performance in the presence of execution"
  Abstract: Path-oriented scheduling methods, such as trace scheduling and hyperblock scheduling, use speculation to extract instruction-level parallelism from control-intensive programs. These methods predict important execution paths in the current scheduling scope using execution profiling or frequency estimation. Aggressive speculation is then applied to the important execution paths, possibly at the cost of degraded performance along other paths. Therefore, the speed of the output code can be sensitive to the compiler's ability to accurately predict the important execution paths. Prior work in this area has utilized the speculative yield function by Fisher, coupled with dependence height, to distribute instruction priority among execution paths in the scheduling scope. While this technique provides more stability of performance by paying attention to the needs of all paths, it does not directly address the problem of mismatch between compile-time prediction and run-time behavior. The work presented in this paper extends the speculative yield and dependence height heuristic to explicitly minimize the penalty suffered by other paths when instructions are speculated along a path. Since the execution time of a path is determined by the number of cycles spent between a path's entrance and exit in the scheduling scope, the heuristic attempts to eliminate unnecessary speculation that delays any path's exit. Such control of speculation makes the performance much less sensitive to the actual path taken at run time. The proposed method has a strong emphasis on achieving minimal delay to all exits. Thus the name, speculative hedge, is used. This paper presents the speculative hedge heuristic, and shows how it controls over-speculation in a superblock/hyperblock scheduler. The stability of out Copyright 1996 IEEE. Published in the Proceedings of the 29th Annual International Symposium on Microarchitecture, De-cember 2-4, 1996, Paris, France. Personal use of this material is permitted. However, permission to reprint/republish this material for resale or redistribution purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the IEEE. Contact: Manager, Copyrights and Permissions / IEEE Service Center / 445 Hoes Lane / P.O. Box 1331 / Piscataway, NJ 08855-1331, USA. Telephone: + Intl. 908-562-3966

Connected through hyperedge HE-1 (induced by source paper "Profile-Driven Instruction Level Parallel Scheduling with Application to Super Blocks"):
  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