human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Instructions"):
  Abstract: Paper and BibTeX entry are available at http://www.complang.tuwien.ac.at/papers/. This paper was published in: Compiler Construction (CC '94), Springer LNCS 786, 1994, pages 158-171 Delayed Exceptions | Speculative Execution of Abstract. Superscalar processors, which execute basic blocks sequentially, cannot use much instruction level parallelism. Speculative execution has been proposed to execute basic blocks in parallel. A pure software approach suffers from low performance, because exception-generating instructions cannot be executed speculatively. We propose delayed exceptions, a combination of hardware and compiler extensions that can provide high performance and correct exception handling in compiler-based speculative execution. Delayed exceptions exploit the fact that exceptions are rare. The compiler assumes the typical case (no exceptions), schedules the code accordingly, and inserts run-time checks and fix-up code that ensure correct execution when exceptions do happen.

Includes members:
  Member "The Limits of Instruction Level Parallelism in SPEC95 Applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "248 Efficient Superscalar Performance Through Boosting")
  Member "Limits of Control Flow on Parallelism":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Control Flow Prediction For Dynamic ILP Processors")
  Member "248 Efficient Superscalar Performance Through Boosting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "GURRR: A Global Unified Resource Requirements Representation")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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