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: "A Comparison of Full and Partial Predicated Execution Support for ILP Processors"
  Abstract: One can effectively utilize predicated execution to improve branch handling in instruction-level parallel processors. Although the potential benefits of predicated execution are high, the tradeoffs involved in the design of an instruction set to support predicated execution can be difficult. On one end of the design spectrum, architectural support for full predicated execution requires increasing the number of source operands for all instructions. Full predicate support provides for the most flexibility and the largest potential performance improvements. On the other end, partial predicated execution support, such as conditional moves, requires very little change to existing architectures. This paper presents a preliminary study to qualitatively and quantitatively address the benefit of full and partial predicated execution support. With our current compiler technology, we show that the compiler can use both partial and full predication to achieve speedup in large control-intensive programs. Some details of the code generation techniques are shown to provide insight into the benefit of going from partial to full predication. Preliminary experimental results are very encouraging: partial predication provides an average of 33% performance improvement for an 8-issue processor with no predicate support while full predication provides an additional 30% improvement.

Connected through hyperedge HE-1 (induced by source paper "Dynamic Hammock Predication for Non-predicated Instruction Set Architectures"):
  with members:
    - "Exploiting Choice: Instruction Fetch and Issue on an Implementable Simultaneous Multithreading Processor"
    - "Limited Dual Path Execution"
    - "Selective Eager Execution on the PolyPath Architecture"
    - "Confidence Estimation for Speculation Control"

Connected through hyperedge HE-2 (induced by source paper "The Limits of Instruction Level Parallelism in SPEC95 Applications"):
  with members:
    - "THE EXPANDABLE SPLIT WINDOW PARADIGM FOR EXPLOITING FINE-GRAIN PARALLELISM"
    - "d d Techniques for Extracting Instruction Level Parallelism on MIMD Architectures"
    - "d d Code Scheduling for Multiple Instruction Stream Architectures"
    - "Limits of Control Flow on Parallelism"
    - "Instructions"
    - "248 Efficient Superscalar Performance Through Boosting"
    - "Theoretical Modeling of Superscalar Processor Performance"

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