human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "edge ai on demand accelerating deep neural network inference via edge computing"):
  with members:
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "jointdnn an efficient training and inference engine for intelligent mobile cloud computing services"
    - "musical chair efficient real time recognition using collaborative iot devices"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "efficient resource allocation for on demand mobile edge cloud computing"
    - "efficient neural architecture search via parameter sharing"
    - "progressive neural architecture search"
    - "a survey on mobile edge computing the communication perspective"

Question: Please tell me which class the center node 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)