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 "d d MISC: A Multiple Instruction Stream Computer"):
  Abstract: This paper describes a single chip Multiple Instruction Stream Computer (MISC) capable of extracting instruction level parallelism from a broad spectrum of programs. The MISC architecture uses multiple asynchronous processing elements to separate a program into streams that can be executed in parallel, and integrates a conflict-free message passing system into the lowest level of the processor design to facilitate low latency intra-MISC communication. This approach allows for increased machine parallelism with minimal code expansion, and provides an alternative approach to single instruction stream multi-issue machines such as SuperScalar and VLIW.

Includes members:
  Member "Converting Thread-Level Parallelism to Instruction-Level Parallelism via Simultaneous Multithreading":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "d d Techniques for Extracting Instruction Level Parallelism on MIMD Architectures")
  Member "d d Techniques for Extracting Instruction Level Parallelism on MIMD Architectures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Converting Thread-Level Parallelism to Instruction-Level Parallelism via Simultaneous Multithreading")
  Member "d d Code Scheduling for Multiple Instruction Stream Architectures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "The Limits of Instruction Level Parallelism in SPEC95 Applications")

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