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: "progressive deep neural networks acceleration via soft filter pruning"
  Abstract: This paper proposed a Progressive Soft Filter Pruning method (PSFP) to prune the filters of deep Neural Networks which can thus be accelerated in the inference. Specifically, the proposed PSFP method prunes the network progressively and enables the pruned filters to be updated when training the model after pruning. PSFP has three advantages over previous works: 1) Larger model capacity. Updating previously pruned filters provides our approach with larger optimization space than fixing the filters to zero. Therefore, the network trained by our method has a larger model capacity to learn from the training data. 2) Less dependence on the pre-trained model. Large capacity enables our method to train from scratch and prune the model simultaneously. In contrast, previous filter pruning methods should be conducted on the basis of the pre-trained model to guarantee their performance. Empirically, PSFP from scratch outperforms the previous filter pruning methods. 3) Pruning the neural network progressively makes the selection of low-norm filters much more stable, which has a potential to get a better performance. Moreover, our approach has been demonstrated effective for many advanced CNN architectures. Notably, on ILSCRC-2012, our method reduces more than 42% FLOPs on ResNet-101 with even 0.2% top-5 accuracy improvement, which has advanced the state-of-the-art. On ResNet-50, our progressive pruning method have 1.08% top-1 accuracy improvement over the pruning method without progressive pruning.

Connected through hyperedge HE-1 (induced by source paper "progressive gradient pruning for classification detection and domainadaptation"):
  with members:
    - "importance estimation for neural network pruning"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "structured pruning of neural networks with budget aware regularization"
    - "yolov3 an incremental improvement"
    - "sbnet sparse blocks network for fast inference"
    - "discrimination aware channel pruning for deep neural networks"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"

Connected through hyperedge HE-2 (induced by source paper "a programmable approach to model compression"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "discrimination aware channel pruning for deep neural networks"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"
    - "attention is all you need"
    - "model compression as constrained optimization with application to neural nets part i general framework"
    - "thinet a filter level pruning method for deep neural network compression"
    - "condensenet an efficient densenet using learned group convolutions"

Connected through hyperedge HE-3 (induced by source paper "a survey of pruning methods for efficient person re identification across domains"):
  with members:
    - "play and prune adaptive filter pruning for deep model compression"
    - "building efficient convnets using redundant feature pruning"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "end to end comparative attention networks for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "an entropy based pruning method for cnn compression"
    - "channel pruning for accelerating very deep neural networks"
    - "gated siamese convolutional neural network architecture for human re identification"

Connected through hyperedge HE-4 (induced by source paper "importance estimation for neural network pruning"):
  with members:
    - "faster gaze prediction with dense networks and fisher pruning"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "rethinking the value of network pruning"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"
    - "data driven sparse structure selection for deep neural networks"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-5 (induced by source paper "super accurate low latency object detection on a surveillance uav"):
  with members:
    - "ifq net integrated fixed point quantization networks for embedded vision"
    - "vision meets drones a challenge"
    - "object detection at 200 frames per second"
    - "yolov3 an incremental improvement"
    - "the unmanned aerial vehicle benchmark object detection and tracking"
    - "highly efficient 8 bit low precision inference of convolutional neural networks with intelcaffe"
    - "focal loss for dense object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-6 (induced by source paper "channel wise pruning of neural networks with tapering resource constraint"):
  with members:
    - "faster gaze prediction with dense networks and fisher pruning"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "systematic weight pruning of dnns using alternating direction method of multipliers"
    - "the incredible shrinking neural network new perspectives on learning representations through the lens of pruning"
    - "structured probabilistic pruning for convolutional neural network acceleration"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "towards thinner convolutional neural networks through gradually global pruning"

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)