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: "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.

Connected through hyperedge HE-1 (induced by source paper "d d Techniques for Extracting Instruction Level Parallelism on MIMD Architectures"):
  with members:
    - "The Limits of Instruction Level Parallelism in SPEC95 Applications"
    - "Converting Thread-Level Parallelism to Instruction-Level Parallelism via Simultaneous Multithreading"

Connected through hyperedge HE-2 (induced by source paper "Converting Thread-Level Parallelism to Instruction-Level Parallelism via Simultaneous Multithreading"):
  with members:
    - "Exploiting Choice: Instruction Fetch and Issue on an Implementable Simultaneous Multithreading Processor"
    - "d d Techniques for Extracting Instruction Level Parallelism on MIMD Architectures"

Connected through hyperedge HE-3 (induced by source paper "d d Code Scheduling for Multiple Instruction Stream Architectures"):
  with members:
    - "The Limits of Instruction Level Parallelism in SPEC95 Applications"

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