You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: We proposed meta-path prediction as self-supervised auxiliary tasks on heterogeneous graphs.Our experiments show that the representation learning on heterogeneous graphscan benefit from meta-path prediction which encourages to capture rich semantic information.The auxiliary tasks can be further improved by our proposed method SELAR, which automatically balances auxiliary tasks to assist the primary task via a form of meta-learning.The learnt weighting function identifies more beneficial meta-paths for the primary tasks.Within a task, the weighting function can adjust the cross entropy like the focal loss, which focuses on hard examples by decreasing weights for easy samples.Moreover, when it comes to challenging and remotely relevant auxiliary tasks,our HintNet helps the learner by correcting the learner’s answer dynamically and further improves the gain from auxiliary tasks.Our framework based on meta-learning provides learning strategies to balance primary task and auxiliary tasks, and easy/hard (and positive/negative) samples.Interesting future directions include applying our framework to other domains and various auxiliary tasks.Our code is publicly available at https://github.com/mlvlab/SELAR. The goal of our framework is to learn with multiple auxiliary tasks to improve the performance of the primary task.In this work, we demonstrate our framework with meta-path predictions as auxiliary tasks. But our framework could be extended to include other auxiliary tasks.The meta-paths capture diverse and meaningful relations between nodes on heterogeneous graphs HAN .However, learning with auxiliary tasks has multiple challenges: identifying useful auxiliary tasks, balancing the auxiliary tasks with the primary task, and converting challenging auxiliary tasks into solvable (and relevant) tasks.To address the challenges, we propose SELf-supervised Auxiliary LeaRning (SELAR).Our framework consists of two main components:1) learning weight functions to softly select auxiliary tasks and balance them with the primary task via meta-learning, and2) learning Hint Networks to convert challenging auxiliary tasks into more relevant and solvable tasks to the primary task learner.

Question: Have the authors experimented with extending to other auxiliary tasks other than meta-path prediction?
In this paper, the authors did not conduct experiments on extending the framework to other auxiliary tasks besides meta-path prediction. However, the authors mention that it is a possible direction for future work.