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: "coordinating filters for faster deep neural networks"
  Abstract: Very large-scale Deep Neural Networks (DNNs) have achieved remarkable successes in a large variety of computer vision tasks. However, the high computation intensity of DNNs makes it challenging to deploy these models on resource-limited systems. Some studies used low-rank approaches that approximate the filters by low-rank basis to accelerate the testing. Those works directly decomposed the pre-trained DNNs by Low-Rank Approximations (LRA). How to train DNNs toward lower-rank space for more efficient DNNs, however, remains as an open area. To solve the issue, in this work, we propose Force Regularization, which uses attractive forces to enforce filters so as to coordinate more weight information into lower-rank space. We mathematically and empirically verify that after applying our technique, standard LRA methods can reconstruct filters using much lower basis and thus result in faster DNNs. The effectiveness of our approach is comprehensively evaluated in ResNets, AlexNet, and GoogLeNet. In AlexNet, for example, Force Regularization gains 2x speedup on modern GPU without accuracy loss and 4.05x speedup on CPU by paying small accuracy degradation. Moreover, Force Regularization better initializes the low-rank DNNs such that the fine-tuning can converge faster toward higher accuracy. The obtained lower-rank DNNs can be further sparsified, proving that Force Regularization can be integrated with state-of-the-art sparsity-based acceleration methods. Source code is available in this https URL

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 "ternary mobilenets via per layer hybrid filter banks"):
  with members:
    - "ternary hybrid neural tree networks for highly constrained iot applications"
    - "compressing rnns for iot devices by 15 38x using kronecker products"
    - "searching for mobilenetv3"
    - "run time efficient rnn compression for inference on edge devices"
    - "pushing the limits of rnn compression"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "amc automl for model compression and acceleration on mobile devices"

Connected through hyperedge HE-3 (induced by source paper "expandnets linear over parameterization to train compact convolutional networks"):
  with members:
    - "acnet strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks"
    - "the impact of neural network overparameterization on gradient confusion and stochastic gradient descent"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "yolo lite a real time object detection algorithm optimized for non gpu computers"
    - "yolov3 an incremental improvement"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "learning deep representations with probabilistic knowledge transfer"

Connected through hyperedge HE-4 (induced by source paper "filter distillation for network compression"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "democratizing production scale distributed deep learning"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "extreme network compression via filter group approximation"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"
    - "domain adaptive deep network compression"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-5 (induced by source paper "all you need is a few shifts designing efficient convolutional neural networks for image classification"):
  with members:
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a layer decomposition recomposition framework for neuron pruning towards accurate lightweight networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "constructing fast network through deconstruction of convolution"
    - "darts differentiable architecture search"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "progressive neural architecture search"

Connected through hyperedge HE-6 (induced by source paper "distilling object detectors with fine grained feature imitation"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "focal loss for dense object detection"
    - "what can help pedestrian detection"
    - "cascade r cnn delving into high quality object detection"
    - "thinet a filter level pruning method for deep neural network compression"
    - "darkrank accelerating deep metric learning via cross sample similarities transfer"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "like what you like knowledge distill via neuron selectivity transfer"

Connected through hyperedge HE-7 (induced by source paper "scalable deep neural networks via low rank matrix factorization"):
  with members:
    - "compression based bound for non compressed network unified generalization error analysis of large compressible deep neural network"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "scan a scalable neural networks framework towards compact and efficient models"
    - "universally slimmable networks and improved training techniques"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "slimmable neural networks"
    - "trace norm regularization and faster inference for embedded speech recognition rnns"

Connected through hyperedge HE-8 (induced by source paper "separable convolutional eigen filters scef building efficient cnns using redundancy analysis"):
  with members:
    - "learning to prune filters in convolutional neural networks"
    - "network compression using correlation analysis of layer responses"
    - "extreme network compression via filter group approximation"
    - "nisp pruning networks using neuron importance score propagation"
    - "thinet a filter level pruning method for deep neural network compression"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "an entropy based pruning method for cnn compression"
    - "channel pruning for accelerating very deep neural 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)