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 "bottlenet an end to end approach for feature compression in device edge co inference systems"):
  Abstract: The emergence of various intelligent mobile applications demands the deployment of powerful deep learning models at resource-constrained mobile devices. The device-edge co-inference framework provides a promising solution by splitting a neural network at a mobile device and an edge computing server. In order to balance the on-device computation and the communication overhead, the splitting point needs to be carefully picked, while the intermediate feature needs to be compressed before transmission. Existing studies decoupled the design of model splitting, feature compression, and communication, which may lead to excessive resource consumption of the mobile device. In this paper, we introduce an end-to-end architecture, named BottleNet++, that consists of an encoder, a non-trainable channel layer, and a decoder for more efficient feature compression and transmission. The encoder and decoder essentially implement joint source-channel coding via convolutional neural networks (CNNs), while explicitly considering the effect of channel noise. By exploiting the strong sparsity and the fault-tolerant property of the intermediate feature in a deep neural network (DNN), BottleNet++ achieves a much higher compression ratio than existing methods. Furthermore, by providing the channel condition to the encoder as an input, our method enjoys a strong generalization ability in different channel conditions. Compared with merely transmitting intermediate data without feature compression, BottleNet++ achieves up to 64x bandwidth reduction over the additive white Gaussian noise channel and up to 256x bit compression ratio in the binary erasure channel, with less than 2% reduction in accuracy. With a higher compression ratio, BottleNet++ enables splitting a DNN at earlier layers, which leads to up to 3x reduction in on-device computation compared with other compression methods.

Includes members:
  Member "improving device edge cooperative inference of deep learning via 2 step pruning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep joint transmission recognition for power constrained iot devices")
  Member "bottlenet a deep learning architecture for intelligent mobile cloud computing services":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "run time deep model multiplexing")
  Member "jointdnn an efficient training and inference engine for intelligent mobile cloud computing services":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "run time deep model multiplexing")
  Member "deep feature compression for collaborative object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "run time deep model multiplexing")
  Member "neural joint source channel coding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distortion agnostic deep watermarking")
  Member "jalad joint accuracy and latency aware deep structure decoupling for edge cloud execution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "measurement driven analysis of an edge assisted object recognition system")
  Member "edge host partitioning of deep neural networks with feature space encoding for resource constrained internet of things platforms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relevant features based auxiliary cells for energy efficient detection of natural errors")
  Member "densely connected convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "lossless compression of deep neural networks")

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)