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 "cross channel intragroup sparsity neural network"):
  Abstract: Modern deep neural network models generally build upon heavy over-parameterization for their exceptional performance. Network pruning is one often employed approach to obtain less demanding models for their deployment. Fine-grained pruning, while can achieve good model compression ratio, introduces irregularity in the computing data flow, often does not give improved model inference efficiency. Coarse-grained model pruning, while allows good inference speed through removing network weights in whole groups, for example, a whole filter, can lead to significant model performance deterioration. In this study, we introduce the cross-channel intragroup (CCI) sparsity structure that can avoid the inference inefficiency of fine-grained pruning while maintaining outstanding model performance.

Includes members:
  Member "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "soft threshold weight reparameterization for learnable sparsity")
  Member "igcv3 interleaved low rank group convolutions for efficient deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "xsepconv extremely separated convolution")
  Member "dnn dataflow choice is overrated":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dnn chip predictor an analytical performance predictor for dnn accelerators with various dataflows and hardware architectures")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "understanding the disharmony between dropout and batch normalization by variance shift":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "salsanext fast semantic segmentation of lidar point clouds for autonomous driving")
  Member "shufflenet v2 practical guidelines for efficient cnn architecture design":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "channelnets compact and efficient convolutional neural networks via channel wise convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inner imaging networks put lenses into convolutional structure")
  Member "block sparse recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "block wise dynamic sparseness")

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)