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 "intermittent learning on device machine learning on intermittently powered system"):
  Abstract: This paper introduces intermittent learning - the goal of which is to enable energy harvested computing platforms capable of executing certain classes of machine learning tasks effectively and efficiently. We identify unique challenges to intermittent learning relating to the data and application semantics of machine learning tasks, and to address these challenges, we devise 1) an algorithm that determines a sequence of actions to achieve the desired learning objective under tight energy constraints, and 2) propose three heuristics that help an intermittent learner decide whether to learn or discard training examples at run-time which increases the energy efficiency of the system. We implement and evaluate three intermittent learning applications that learn the 1) air quality, 2) human presence, and 3) vibration using solar, RF, and kinetic energy harvesters, respectively. We demonstrate that the proposed framework improves the energy efficiency of a learner by up to 100% and cuts down the number of learning examples by up to 50% when compared to state-of-the-art intermittent computing systems that do not implement the proposed intermittent learning framework.

Includes members:
  Member "alpaca intermittent execution without checkpoints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "zygarde time sensitive on device deep intelligence on intermittently powered systems")
  Member "intelligence beyond the edge inference on intermittent embedded systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "regularizing activation distribution for training binarized deep networks")
  Member "deepiot compressing deep neural network structures for sensing systems with a compressor critic framework":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recurrent neural networks an embedded computing perspective")
  Member "lightnn filling the gap between conventional deep neural networks and binarized networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pops policy pruning and shrinking for deep reinforcement learning")
  Member "sparse neural networks topologies":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pre defined sparse neural networks with hardware acceleration")
  Member "dcnns on a diet sampling strategies for reducing the training set size":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "progressive compressed records taking a byte out of deep learning data")
  Member "binaryconnect training deep neural networks with binary weights during propagations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sparse weight activation training")
  Member "deep compression compressing deep neural networks with pruning trained quantization and huffman coding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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.LG(Machine Learning)