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: "Dynamic Hammock Predication for Non-predicated Instruction Set Architectures"
  Abstract: Conventional speculative architectures use branch prediction to evaluate the most likely execution path during program execution. However, certain branches are difficult to predict. One solution to this problem is to evaluate both paths following such a conditional branch. Predicated execution can be used to implement this form of multi-path execution. Predicated architectures fetch and issue instructions that have associated predicates. These predicates indicate if the instruction should commit its result. Predicating a branch reduces the number of branches executed, eliminating the chance of branch misprediction at the cost of executing additional instructions. In this paper, we propose a restricted form of multi-path execution called Dynamic Predication for architectures with little or no support for predicated instructions in their instruction set. Dynamic predication dynamically predicates instruction sequences in the form of a branch hammock, concurrently executing both paths of the branch. A branch hammock is a short forward branch that spans a few instructions in the form of an if-then or if-then-else construct. We mark these and other constructs in the executable. When the decode stage detects such a sequence, it passes a predicated instruction sequence to a dynamically scheduled execution core. Our results show that dynamic predication can accrue speedups of up to 13%.

Connected through hyperedge HE-1 (induced by source paper "Confidence Estimation for Speculation Control"):
  with members:
    - "Limited Dual Path Execution"
    - "Selective Eager Execution on the PolyPath Architecture"

Connected through hyperedge HE-2 (induced by source paper "A Comparison of Full and Partial Predicated Execution Support for ILP Processors"):
  with members:
    - "The Limits of Instruction Level Parallelism in SPEC95 Applications"

Connected through hyperedge HE-3 (induced by source paper "Selective Eager Execution on the PolyPath Architecture"):
  with members:
    - "Multipath Execution: Opportunities and Limits"
    - "Threaded Multiple Path Execution"
    - "Limited Dual Path Execution"
    - "Confidence Estimation for Speculation Control"

Connected through hyperedge HE-4 (induced by source paper "Exploiting Choice: Instruction Fetch and Issue on an Implementable Simultaneous Multithreading Processor"):
  with members:
    - "Multipath Execution: Opportunities and Limits"
    - "Threaded Multiple Path Execution"
    - "Converting Thread-Level Parallelism to Instruction-Level Parallelism via Simultaneous Multithreading"

Connected through hyperedge HE-5 (induced by source paper "Limited Dual Path Execution"):
  with members:
    - "Multipath Execution: Opportunities and Limits"
    - "Threaded Multiple Path Execution"
    - "d d The Effects of Predicated Execution on Branch Prediction"
    - "Selective Eager Execution on the PolyPath Architecture"
    - "Confidence Estimation for Speculation Control"

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