human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "a spike in performance training hybrid spiking neural networks with quantized activation functions"):
  with members:
    - "recognizing images with at most one spike per neuron"
    - "improving efficiency in neural network accelerator using operands hamming distance optimization"
    - "bit efficient quantization for deep neural networks"
    - "cold case the lost mnist digits"
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "stochastic rounding and reduced precision fixed point arithmetic for solving neural odes"
    - "spike train level backpropagation for training deep recurrent spiking neural networks"
    - "energy aware neural architecture optimization with fast splitting steepest descent"

Connected through hyperedge HE-2 (induced by source paper "learning instance wise sparsity for accelerating deep models"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "sbnet sparse blocks network for fast inference"
    - "dynamic channel pruning feature boosting and suppression"
    - "skipnet learning dynamic routing in convolutional networks"
    - "learning efficient convolutional networks through network slimming"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "adaptive neural networks for efficient inference"
    - "branchynet fast inference via early exiting from deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "self adaptive network pruning"):
  with members:
    - "autopruner an end to end trainable filter pruning method for efficient deep model inference"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "discrete autoencoders for sequence models"
    - "dynamic channel pruning feature boosting and suppression"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"

Connected through hyperedge HE-4 (induced by source paper "dasnet dynamic activation sparsity for neural network efficiency improvement"):
  with members:
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "dynamic channel pruning feature boosting and suppression"
    - "network dissection quantifying interpretability of deep visual representations"
    - "in datacenter performance analysis of a tensor processing unit"
    - "learning efficient convolutional networks through network slimming"
    - "data driven sparse structure selection for deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "compressing dma engine leveraging activation sparsity for training deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "dynamic channel pruning feature boosting and suppression"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "discrimination aware channel pruning for deep neural networks"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "more is less a more complicated network with less inference complexity"
    - "channel pruning for accelerating very deep neural networks"
    - "learning structured sparsity in deep neural networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "perforatedcnns acceleration through elimination of redundant convolutions"

Question: Please tell me which class the center node 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)