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: "recovering from random pruning on the plasticity of deep convolutional neural networks"
  Abstract: Recently there has been a lot of work on pruning filters from deep convolutional neural networks (CNNs) with the intention of reducing computations. The key idea is to rank the filters based on a certain criterion (say, $l_1$-norm, average percentage of zeros, etc) and retain only the top ranked filters. Once the low scoring filters are pruned away the remainder of the network is fine tuned and is shown to give performance comparable to the original unpruned network. In this work, we report experiments which suggest that the comparable performance of the pruned network is not due to the specific criterion chosen but due to the inherent plasticity of deep neural networks which allows them to recover from the loss of pruned filters once the rest of the filters are fine-tuned. Specifically, we show counter-intuitive results wherein by randomly pruning 25-50\% filters from deep CNNs we are able to obtain the same performance as obtained by using state of the art pruning methods. We empirically validate our claims by doing an exhaustive evaluation with VGG-16 and ResNet-50. Further, we also evaluate a real world scenario where a CNN trained on all 1000 ImageNet classes needs to be tested on only a small set of classes at test time (say, only animals). We create a new benchmark dataset from ImageNet to evaluate such class specific pruning and show that even here a random pruning strategy gives close to state of the art performance. Lastly, unlike existing approaches which mainly focus on the task of image classification, in this work we also report results on object detection. We show that using a simple random pruning strategy we can achieve significant speed up in object detection (74$\%$ improvement in fps) while retaining the same accuracy as that of the original Faster RCNN model.

Connected through hyperedge HE-1 (induced by source paper "pruning filters while training for efficiently optimizing deep learning networks"):
  with members:
    - "pca driven hybrid network design for enabling intelligence at the edge"
    - "rethinking the value of network pruning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "pruning convolutional neural networks for resource efficient inference"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "reducing transformer depth on demand with structured dropout"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "adaptively sparse transformers"
    - "very deep self attention networks for end to end speech recognition"
    - "pay less attention with lightweight and dynamic convolutions"
    - "learning deep transformer models for machine translation"
    - "pre trained language model representations for language generation"

Connected through hyperedge HE-3 (induced by source paper "pack and detect fast object detection in videos using region of interest packing"):
  with members:
    - "yolov3 an incremental improvement"
    - "recurrent residual module for fast inference in videos"
    - "object detection in videos by short and long range object linking"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "fast yolo a fast you only look once system for real time embedded object detection in video"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "towards high performance video object detection"
    - "flow guided feature aggregation for video object detection"

Connected through hyperedge HE-4 (induced by source paper "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "training wide residual networks for deployment using a single bit for each weight"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "network compression using correlation analysis of layer responses"
    - "data driven sparse structure selection for deep neural networks"
    - "theory of deep learning iib optimization properties of sgd"

Connected through hyperedge HE-5 (induced by source paper "efficient video classification using fewer frames"):
  with members:
    - "less is more picking informative frames for video captioning"
    - "non local netvlad encoding for video classification"
    - "constrained size tensorflow models for youtube 8m video understanding challenge"
    - "nextvlad an efficient neural network to aggregate frame level features for large scale video classification"
    - "knowledge distillation by on the fly native ensemble"
    - "attention guided answer distillation for machine reading comprehension"
    - "learnable pooling methods for video classification"
    - "label denoising with large ensembles of heterogeneous neural networks"

Connected through hyperedge HE-6 (induced by source paper "make ell_1 regularization effective in training sparse cnn"):
  with members:
    - "rethinking the value of network pruning"
    - "learning efficient convolutional networks through network slimming"
    - "data driven sparse structure selection for deep neural networks"
    - "thinet a filter level pruning method for deep neural network compression"
    - "a survey of model compression and acceleration for deep neural networks"
    - "channel pruning for accelerating very deep neural networks"
    - "learning structured sparsity in deep neural networks"
    - "network trimming a data driven neuron pruning approach towards efficient deep architectures"

Connected through hyperedge HE-7 (induced by source paper "differentiable mask pruning for neural networks"):
  with members:
    - "differentiable pruning method for neural networks"
    - "discrimination aware channel pruning for deep neural networks"
    - "neural rejuvenation improving deep network training by enhancing computational resource utilization"
    - "bnn improved binary network training"
    - "learning efficient convolutional networks through network slimming"
    - "data driven sparse structure selection for deep neural networks"
    - "thinet a filter level pruning method for deep neural network compression"
    - "learning structured sparsity in deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "rethinking the value of network pruning"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "network compression using correlation analysis of layer responses"
    - "darts differentiable architecture search"
    - "layer compensated pruning for resource constrained convolutional neural networks"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "efficient neural architecture search via parameter sharing"

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)