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: "scsp spectral clustering filter pruning with soft self adaption manners"
  Abstract: Deep Convolutional Neural Networks (CNN) has achieved significant success in computer vision field. However, the high computational cost of the deep complex models prevents the deployment on edge devices with limited memory and computational resource. In this paper, we proposed a novel filter pruning for convolutional neural networks compression, namely spectral clustering filter pruning with soft self-adaption manners (SCSP). We first apply spectral clustering on filters layer by layer to explore their intrinsic connections and only count on efficient groups. By self-adaption manners, the pruning operations can be done in few epochs to let the network gradually choose meaningful groups. According to this strategy, we not only achieve model compression while keeping considerable performance, but also find a novel angle to interpret the model compression process.

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"
    - "exploring linear relationship in feature map subspace for convnets compression"
    - "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"
    - "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 "meta filter pruning to accelerate deep convolutional neural networks"):
  with members:
    - "exploring linear relationship in feature map subspace for convnets compression"
    - "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"
    - "network compression using correlation analysis of layer responses"
    - "layer compensated pruning for resource constrained convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "functionality oriented convolutional filter pruning"):
  with members:
    - "sim real joint reinforcement transfer for 3d indoor navigation"
    - "revisiting the importance of individual units in cnns via ablation"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "rethinking the value of network pruning"
    - "nisp pruning networks using neuron importance score propagation"
    - "channel pruning for accelerating very deep neural networks"
    - "learning structured sparsity in deep neural networks"

Connected through hyperedge HE-4 (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"
    - "exploring linear relationship in feature map subspace for convnets compression"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "interpretable convolutional filter pruning"):
  with members:
    - "sim real joint reinforcement transfer for 3d indoor navigation"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "revisiting the importance of individual units in cnns via ablation"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "rethinking the value of network pruning"
    - "how convolutional neural network see the world a survey of convolutional neural network visualization methods"

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)