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 "high throughput cnn inference on embedded arm big little multi core processors"):
  Abstract: IoT Edge intelligence requires Convolutional Neural Network (CNN) inference to take place in the edge device itself. ARM big.LITTLE architecture is at the heart of common commercial edge devices. It comprises of single-ISA heterogeneous multi-cores grouped in homogeneous clusters that enables performance and power trade-offs. However, high communication overhead involved in parallelization of computation from a convolution kernel across clusters is detrimental to throughput. We present an alternative framework called Pipe-it that employs a pipelined design to split the convolutional layers across clusters while limiting the parallelization of their respective kernels to the assigned clusters. We develop a performance prediction model that, from convolutional layer descriptors, predicts the execution time of each layer individually on all different core types and number of cores. Pipe-it then exploits the predictions to create a balanced pipeline using an efficient design space exploration algorithm. Pipe-it on average results in 39% higher throughput than the highest antecedent throughput.

Includes members:
  Member "netadapt platform aware neural network adaptation for mobile applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "euphrates algorithm soc co design for low power mobile continuous vision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "balancing efficiency and flexibility for dnn acceleration via temporal gpu systolic array integration")
  Member "tvm end to end optimization stack for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "torch struct deep structured prediction library")
  Member "ai benchmark running deep neural networks on android smartphones":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "replacing mobile camera isp with a single deep learning model")
  Member "mobile machine learning hardware at arm a systems on chip soc perspective":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "flexibo cost aware multi objective optimization of deep neural networks")
  Member "scheduling computation graphs of deep learning models on manycore cpus":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimizing cnn model inference on cpus")
  Member "optimizing cnn model inference on cpus":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the deep learning compiler a comprehensive survey")
  Member "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "aggressive perception aware navigation using deep optical flow dynamics and pixelmpc")

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)