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 "an inter layer weight prediction and quantization for deep neural networks based on a smoothly varying weight hypothesis"):
  Abstract: Network compression for deep neural networks has become an important part of deep learning research, because of increased demand for deep learning models in practical resource-constrained environments. In this paper, we observe that the weights in adjacent convolution layers share strong similarity in shapes and values, i.e., the weights tend to vary smoothly along the layers. We call this phenomenon \textit{Smoothly Varying Weight Hypothesis} (SVWH). Based on SVWH and an inter-frame prediction method in conventional video coding schemes, we propose a new \textit{Inter-Layer Weight Prediction} (ILWP) and quantization method which quantize the predicted residuals of the weights. Since the predicted weight residuals tend to follow Laplacian distributions with very low variance, the weight quantization can more effectively be applied, thus producing more zero weights and enhancing weight compression ratio. In addition, we propose a new loss for eliminating non-texture bits, which enabled us to more effectively store only texture bits. That is, the proposed loss regularizes the weights such that the collocated weights between the adjacent two layers have the same values. Our comprehensive experiments show that the proposed method achieved much higher weight compression rate at the same accuracy level compared with the previous quantization-based compression methods in deep neural networks.

Includes members:
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "shufflenet v2 practical guidelines for efficient cnn architecture design":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "shufflenet an extremely efficient convolutional neural network for mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "learning efficient convolutional networks through network slimming":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "the microsoft 2017 conversational speech recognition system":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep segmental phonetic posterior grams based discovery of non categories in l2 english speech")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "depthwise separable convolutions for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hierarchical memory decoding for video captioning")

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)