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: "the power of sparsity in convolutional neural networks"
  Abstract: Deep convolutional networks are well-known for their high computational and memory demands. Given limited resources, how does one design a network that balances its size, training time, and prediction accuracy? A surprisingly effective approach to trade accuracy for size and speed is to simply reduce the number of channels in each convolutional layer by a fixed fraction and retrain the network. In many cases this leads to significantly smaller networks with only minimal changes to accuracy. In this paper, we take a step further by empirically examining a strategy for deactivating connections between filters in convolutional layers in a way that allows us to harvest savings both in run-time and memory for many network architectures. More specifically, we generalize 2D convolution to use a channel-wise sparse connection structure and show that this leads to significantly better results than the baseline approach for large networks including VGG and Inception V3.

Connected through hyperedge HE-1 (induced by source paper "what is the state of neural network pruning"):
  with members:
    - "jointly sparse convolutional neural networks in dual spatial winograd domains"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "benchmark analysis of representative deep neural network architectures"
    - "sprintz time series compression for the internet of things"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "efficient neural audio synthesis"
    - "network compression using correlation analysis of layer responses"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"

Connected through hyperedge HE-2 (induced by source paper "leanconvnets low cost yet effective convolutional neural networks"):
  with members:
    - "imexnet a forward stable deep neural network"
    - "deep learning for audio signal processing"
    - "mixconv mixed depthwise convolutional kernels"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "scalable methods for 8 bit training of neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "diagonalwise refactorization an efficient training method for depthwise convolutions"

Connected through hyperedge HE-3 (induced by source paper "model slicing for supporting complex analytics with elastic inference cost and resource constraints"):
  with members:
    - "isbnet instance aware selective branching networks"
    - "group normalization"
    - "slimmable neural networks"
    - "skipnet learning dynamic routing in convolutional networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"
    - "learning anytime predictions in neural networks via adaptive loss balancing"

Connected through hyperedge HE-4 (induced by source paper "patient knowledge distillation for bert model compression"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "model compression via distillation and quantization"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (induced by source paper "on correlation of features extracted by deep neural networks"):
  with members:
    - "diversity regularized adversarial learning"
    - "deep learning of constrained autoencoders for enhanced understanding of data"
    - "building efficient convnets using redundant feature pruning"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "self normalizing neural networks"
    - "learning structured sparsity in deep neural networks"
    - "wavenet a generative model for raw audio"
    - "dsd dense sparse dense training for deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "learning efficient convolutional networks through irregular convolutional kernels"):
  with members:
    - "learning efficient convolutional networks through network slimming"
    - "irregular convolutional neural networks"
    - "deformable convolutional networks"
    - "channel pruning for accelerating very deep neural networks"
    - "learning structured sparsity in deep neural networks"
    - "ssd single shot multibox detector"
    - "training sparse neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-7 (induced by source paper "compressing rnns for iot devices by 15 38x using kronecker products"):
  with members:
    - "tucker tensor layer in fully connected neural networks"
    - "run time efficient rnn compression for inference on edge devices"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "measuring scheduling efficiency of rnns for nlp applications"
    - "the state of sparsity in deep neural networks"
    - "structured weight matrices based hardware accelerators in deep neural networks fpgas and asics"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"

Connected through hyperedge HE-8 (induced by source paper "magnitude and uncertainty pruning criterion for neural networks"):
  with members:
    - "thinet a filter level pruning method for deep neural network compression"
    - "exploring sparsity in recurrent neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "a survey of model compression and acceleration for deep neural networks"
    - "learning structured sparsity in deep neural networks"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "pruning filters for efficient convnets"

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.CV(Computer Vision and Pattern Recognition)