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: "dynamic channel pruning feature boosting and suppression"
  Abstract: Making deep convolutional neural networks more accurate typically comes at the cost of increased computational and memory resources. In this paper, we reduce this cost by exploiting the fact that the importance of features computed by convolutional layers is highly input-dependent, and propose feature boosting and suppression (FBS), a new method to predictively amplify salient convolutional channels and skip unimportant ones at run-time. FBS introduces small auxiliary connections to existing convolutional layers. In contrast to channel pruning methods which permanently remove channels, it preserves the full network structures and accelerates convolution by dynamically skipping unimportant input and output channels. FBS-augmented networks are trained with conventional stochastic gradient descent, making it readily available for many state-of-the-art CNNs. We compare FBS to a range of existing channel pruning and dynamic execution schemes and demonstrate large improvements on ImageNet classification. Experiments show that FBS can respectively provide $5\times$ and $2\times$ savings in compute on VGG-16 and ResNet-18, both with less than $0.6\%$ top-5 accuracy loss.

Connected through hyperedge HE-1 (induced by source paper "effective training of convolutional neural networks with low bitwidth weights and activations"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "single path one shot neural architecture search with uniform sampling"
    - "precision highway for ultra low precision quantization"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-2 (induced by source paper "bringing giant neural networks down to earth with unlabeled data"):
  with members:
    - "importance estimation for neural network pruning"
    - "from hashing to cnns training binaryweight networks via hashing"
    - "deep predictive coding network for object recognition"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "discrimination aware channel pruning for deep neural networks"
    - "recent advances in efficient computation of deep convolutional neural networks"
    - "exploring the limits of weakly supervised pretraining"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-3 (induced by source paper "dynamic convolutions exploiting spatial sparsity for faster inference"):
  with members:
    - "batch shaping for learning conditional channel gated networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "sbnet sparse blocks network for fast inference"
    - "mnasnet platform aware neural architecture search for mobile"
    - "skipnet learning dynamic routing in convolutional networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "an analysis of deep neural network models for practical applications"
    - "adaptive neural networks for efficient inference"

Connected through hyperedge HE-4 (induced by source paper "s2dnas transforming static cnn model for dynamic inference via neural architecture search"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "sbnet sparse blocks network for fast inference"
    - "darts differentiable architecture search"
    - "mnasnet platform aware neural architecture search for mobile"
    - "skipnet learning dynamic routing in convolutional networks"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"

Connected through hyperedge HE-5 (induced by source paper "stochastic region pooling make attention more expressive"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multi attention multi class constraint for fine grained image recognition"
    - "harmonious attention network for person re identification"
    - "dropblock a regularization method for convolutional networks"
    - "convolutional neural networks with alternately updated clique"
    - "competitive inner imaging squeeze and excitation for residual network"
    - "learning what and where to attend"
    - "gather excite exploiting feature context in convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "butterfly transform an efficient fft based neural architecture design"):
  with members:
    - "learning fast algorithms for linear transforms using butterfly factorizations"
    - "on the expressive power of deep diagonal circulant neural networks"
    - "on the expressive power of deep fully circulant neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "amc automl for model compression and acceleration on mobile devices"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-7 (induced by source paper "steerage synthesis of neural networks using architecture search and grow and prune methods"):
  with members:
    - "incremental learning using a grow and prune paradigm with efficient neural networks"
    - "scann synthesis of compact and accurate neural networks"
    - "single path one shot neural architecture search with uniform sampling"
    - "switchable normalization for learning to normalize deep representation"
    - "secret semantically enhanced classification of real world tasks"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "amc automl for model compression and acceleration on mobile devices"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"

Connected through hyperedge HE-8 (induced by source paper "batch shaping for learning conditional channel gated networks"):
  with members:
    - "sbnet sparse blocks network for fast inference"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "sgad soft guided adaptively dropped neural network"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"
    - "compressing neural networks using the variational information bottleneck"
    - "gaternet dynamic filter selection in convolutional neural network via a dedicated global gating network"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "skipnet learning dynamic routing in convolutional networks"

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)