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: "speeding up convolutional neural networks by exploiting the sparsity of rectifier units"
  Abstract: Rectifier neuron units (ReLUs) have been widely used in deep convolutional networks. An ReLU converts negative values to zeros, and does not change positive values, which leads to a high sparsity of neurons. In this work, we first examine the sparsity of the outputs of ReLUs in some popular deep convolutional architectures. And then we use the sparsity property of ReLUs to accelerate the calculation of convolution by skipping calculations of zero-valued neurons. The proposed sparse convolution algorithm achieves some speedup improvements on CPUs compared to the traditional matrix-matrix multiplication algorithm for convolution when the sparsity is not less than 0.9.

Connected through hyperedge HE-1 (induced by source paper "spatially adaptive inference with stochastic feature sampling and interpolation"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "importance estimation for neural network pruning"
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "sbnet sparse blocks network for fast inference"
    - "an end to end approach for speeding up neural network inference"
    - "deformable convnets v2 more deformable better results"

Connected through hyperedge HE-2 (induced by source paper "sparse deep neural network graph challenge"):
  with members:
    - "radix net structured sparse matrices for deep neural networks"
    - "sparse deep neural network exact solutions"
    - "in datacenter performance analysis of a tensor processing unit"
    - "streaming graph challenge stochastic block partition"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "static graph challenge subgraph isomorphism"
    - "enabling massive deep neural networks with the graphblas"
    - "pagerank pipeline benchmark proposal for a holistic system benchmark for big data platforms"

Connected through hyperedge HE-3 (induced by source paper "blocking and sparsity for optimization of convolution calculation algorithm on gpus"):
  with members:
    - "accelerating training of deep neural networks via sparse edge processing"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "fast algorithms for convolutional neural networks"
    - "cudnn efficient primitives for deep learning"
    - "fast training of convolutional networks through ffts"

Connected through hyperedge HE-4 (induced by source paper "performance optimization of convolution calculation by blocking and sparsity on gpu"):
  with members:
    - "accelerating training of deep neural networks via sparse edge processing"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "fast algorithms for convolutional neural networks"
    - "cudnn efficient primitives for deep learning"
    - "fast training of convolutional networks through ffts"

Connected through hyperedge HE-5 (induced by source paper "recurrent residual module for fast inference in videos"):
  with members:
    - "moments in time dataset one million videos for event understanding"
    - "performance guaranteed network acceleration via high order residual quantization"
    - "what actions are needed for understanding human actions in videos"
    - "predictive corrective networks for action detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "the kinetics human action video dataset"
    - "the power of sparsity in convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "sbnet sparse blocks network for fast inference"):
  with members:
    - "cnvlutin2 ineffectual activation and weight free deep neural network computing"
    - "focal loss for dense object detection"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "ron reverse connection with objectness prior networks for object detection"
    - "submanifold sparse convolutional networks"
    - "sparsity invariant cnns"
    - "more is less a more complicated network with less inference complexity"
    - "channel pruning for accelerating very deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "cascaded coarse to fine deep kernel networks for efficient satellite image change detection"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "a compact dnn approaching googlenet level accuracy of classification and domain adaptation"
    - "the power of sparsity in convolutional neural networks"
    - "learning structured sparsity in deep neural networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "deep kernel learning"

Connected through hyperedge HE-8 (induced by source paper "adam induces implicit weight sparsity in rectifier neural networks"):
  with members:
    - "on the convergence of adam and beyond"
    - "decoupled weight decay regularization"
    - "learning structured sparsity in deep neural networks"
    - "pruning filters for efficient convnets"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"

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)