human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "zygarde time sensitive on device deep intelligence on intermittently powered systems"):
  Abstract: In this paper, we propose a time-, energy-, and accuracy-aware scheduling algorithm for intermittently powered systems that execute compressed deep learning tasks that are suitable for MCUs and are powered solely by harvested energy. The sporadic nature of harvested energy, resource constraints of the embedded platform, and the computational demand of deep neural networks (even though compressed) pose a unique and challenging real-time scheduling problem for which no solutions have been proposed in the literature. We empirically study the problem and model the energy harvesting pattern as well as the trade-off between the accuracy and execution of a deep neural network. We develop an imprecise computing-based scheduling algorithm that improves the schedulability of deep learning tasks on intermittently powered systems. We also utilize the dependency of the computational need of data samples for deep learning models and propose early termination of deep neural networks. We further propose a semi-supervised machine learning model that exploits the deep features and contributes in determining the imprecise partition of a task. We implement our proposed algorithms on two different datasets and real-life scenarios and show that it increases the accuracy by 9.45% - 3.19%, decreases the execution time by 14\% and successfully schedules 33%-12% more tasks.

Includes members:
  Member "alpaca intermittent execution without checkpoints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "intermittent learning on device machine learning on intermittently powered system")
  Member "rethinking floating point for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "shifted and squeezed 8 bit floating point format for low precision training of deep neural networks")
  Member "generalized cross entropy loss for training deep neural networks with noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification")
  Member "fastdeepiot towards understanding and optimizing neural network execution time on mobile and embedded devices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning based pedestrian inertial navigation methods dataset and on device inference")
  Member "adaptive neural networks for efficient inference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distilled hierarchical neural ensembles with adaptive inference cost")
  Member "deepiot compressing deep neural network structures for sensing systems with a compressor critic framework":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "recurrent neural networks an embedded computing perspective")
  Member "rdeepsense reliable deep mobile computing models with uncertainty estimations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep learning at the edge")
  Member "projectionnet learning efficient on device deep networks using neural projections":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "optimizing deep learning inference on embedded systems through adaptive model selection")

Question: Please tell me which class the source paper belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.DC(Distributed, Parallel, and Cluster Computing)