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: "exploring linear relationship in feature map subspace for convnets compression"
  Abstract: While the research on convolutional neural networks (CNNs) is progressing quickly, the real-world deployment of these models is often limited by computing resources and memory constraints. In this paper, we address this issue by proposing a novel filter pruning method to compress and accelerate CNNs. Our work is based on the linear relationship identified in different feature map subspaces via visualization of feature maps. Such linear relationship implies that the information in CNNs is redundant. Our method eliminates the redundancy in convolutional filters by applying subspace clustering to feature maps. In this way, most of the representative information in the network can be retained in each cluster. Therefore, our method provides an effective solution to filter pruning for which most existing methods directly remove filters based on simple heuristics. The proposed method is independent of the network structure, thus it can be adopted by any off-the-shelf deep learning libraries. Experiments on different networks and tasks show that our method outperforms existing techniques before fine-tuning, and achieves the state-of-the-art results after fine-tuning.

Connected through hyperedge HE-1 (induced by source paper "filter grafting for deep neural networks"):
  with members:
    - "omni scale feature learning for person re identification"
    - "importance estimation for neural network pruning"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "network transplanting"
    - "scsp spectral clustering filter pruning with soft self adaption manners"
    - "network compression using correlation analysis of layer responses"
    - "soft filter pruning for accelerating deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "a one step pruning recovery framework for acceleration of convolutional neural networks"):
  with members:
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "learning intrinsic sparse structures within long short term memory"
    - "thinet a filter level pruning method for deep neural network compression"
    - "channel pruning for accelerating very deep neural networks"
    - "stacked hourglass networks for human pose estimation"
    - "learning structured sparsity in deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "progressive learning algorithm for efficient person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "pyramid person matching network for person re identification"
    - "re id done right towards good practices for person re identification"
    - "resource aware person re identification across multiple resolutions"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"

Connected through hyperedge HE-4 (induced by source paper "meta filter pruning to accelerate deep convolutional neural networks"):
  with members:
    - "learning to prune filters in convolutional neural networks"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "leveraging filter correlations for deep model compression"
    - "discrimination aware channel pruning for deep neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "scsp spectral clustering filter pruning with soft self adaption manners"
    - "network compression using correlation analysis of layer responses"
    - "layer compensated pruning for resource constrained convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "leveraging filter correlations for deep model compression"):
  with members:
    - "hetconv heterogeneous kernel based convolutions for deep cnns"
    - "cpwc contextual point wise convolution for object recognition"
    - "play and prune adaptive filter pruning for deep model compression"
    - "stability based filter pruning for accelerating deep cnns"
    - "multi layer pruning framework for compressing single shot multibox detector"
    - "amc automl for model compression and acceleration on mobile devices"
    - "model compression via distillation and quantization"
    - "bayesian model agnostic meta learning"

Connected through hyperedge HE-6 (induced by source paper "filter pruning via geometric median for deep convolutional neural networks acceleration"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "searching for a robust neural architecture in four gpu hours"
    - "sim real joint reinforcement transfer for 3d indoor navigation"
    - "learning to prune filters in convolutional neural networks"
    - "paraphrasing complex network network compression via factor transfer"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "discrimination aware channel pruning for deep neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"

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)