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 "channel gating neural networks"):
  Abstract: Employing deep neural networks to obtain state-of-the-art performance on computer vision tasks can consume billions of floating point operations and several Joules of energy per evaluation. Network pruning, which statically removes unnecessary features and weights, has emerged as a promising way to reduce this computation cost. In this paper, we propose channel gating, a dynamic, fine-grained, training-based computation-cost-reduction scheme. Channel gating works by identifying the regions in the features which contribute less to the classification result and turning off a subset of the channels for computing the pixels within these uninteresting regions. Unlike static network pruning, the channel gating optimizes computations exploiting characteristics specific to each input at run-time. We show experimentally that applying channel gating in state-of-the-art networks can achieve 66% and 60% reduction in FLOPs with 0.22% and 0.29% accuracy loss on the CIFAR-10 and CIFAR-100 datasets, respectively.

Includes members:
  Member "discrimination aware channel pruning for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "soft filter pruning for accelerating deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "dynamic channel pruning feature boosting and suppression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "effective training of convolutional neural networks with low bitwidth weights and activations")
  Member "learning efficient convolutional networks through network slimming":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "an analysis of deep neural network models for practical applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "crl class representative learning for image classification")
  Member "modeling the resource requirements of convolutional neural networks on mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spring a sparsity aware reduced precision monolithic 3d cnn accelerator architecture for training and inference")
  Member "towards accurate binary convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sparse weight activation training")
  Member "blockdrop dynamic inference paths in residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "switchable precision 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)