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: "Converting Thread-Level Parallelism to Instruction-Level Parallelism via Simultaneous Multithreading"
  Abstract: A version of this paper will appear in ACM Transactions on Computer Systems, August 1997. Permission to make digital copies of part or all of this work for personal or classroom use is grantedwithout fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Abstract To achieve high performance, contemporary computer systems rely on two forms of parallelism: instruction-level parallelism (ILP) and thread-level parallelism (TLP). Wide-issue superscalar processors exploit ILP by executing multiple instructions from a single program in a single cycle. Multiprocessors (MP) exploit TLP by executing different threads in parallel on different processors. Unfortunately, both parallel-processing styles statically partition processor resources, thus preventing them from adapting to dynamically-changing levels of ILP and TLP in a program. With insufficient TLP, processors in an MP will be idle; with insufficient ILP, multiple-issue hardware on a superscalar is wasted. This paper explores parallel processing on an alternative architecture, simultaneous multithreading (SMT), which allows multiple threads to compete for and share all of the processors resources every cycle. The most compelling reason for running parallel applications on an SMT processor is its ability to use thread-level parallelism and instruction-level parallelism interchangeably. By permitting multiple threads to share the processors functional units simultaneously, the processor can use both ILP and TLP to accommodate variations in parallelism. When a program has only a single thread, all of the SMT processors resources can be dedicated to that thread; when more TLP exists, this parallelism can compensate for a lack of

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"
    - "d d MISC: A Multiple Instruction Stream Computer"

Connected through hyperedge HE-2 (induced by source paper "Exploiting Choice: Instruction Fetch and Issue on an Implementable Simultaneous Multithreading Processor"):
  with members:
    - "Multipath Execution: Opportunities and Limits"
    - "Dynamic Hammock Predication for Non-predicated Instruction Set Architectures"
    - "Threaded Multiple Path Execution"

Connected through hyperedge HE-3 (induced by source paper "d d MISC: A Multiple Instruction Stream Computer"):
  with members:
    - "d d Techniques for Extracting Instruction Level Parallelism on MIMD Architectures"
    - "d d Code Scheduling for Multiple Instruction Stream Architectures"

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