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: "paraphrasing complex network network compression via factor transfer"
  Abstract: Deep neural networks (DNN) have recently shown promising performances in various areas. Although DNNs are very powerful, a large number of network parameters requires substantial storage and memory bandwidth which hinders them from being applied to actual embedded systems. Many researchers have sought ways of model compression to reduce the size of a network with minimal performance degradation. Among them, a method called knowledge transfer is to train the student network with a stronger teacher network. In this paper, we propose a method to overcome the limitations of conventional knowledge transfer methods and improve the performance of a student network. An auto-encoder is used in an unsupervised manner to extract compact factors which are defined as compressed feature maps of the teacher network. When using the factors to train the student network, we observed that the performance of the student network becomes better than the ones with other conventional knowledge transfer methods because factors contain paraphrased compact information of the teacher network that is easy for the student network to understand.

Connected through hyperedge HE-1 (induced by source paper "knowledge distillation via adaptive instance normalization"):
  with members:
    - "on the efficacy of knowledge distillation"
    - "relational knowledge distillation"
    - "adaptis adaptive instance selection network"
    - "few shot adversarial learning of realistic neural talking head models"
    - "xnor net improved binary neural networks"
    - "a comprehensive overhaul of feature distillation"
    - "contrastive representation distillation"
    - "similarity preserving knowledge distillation"

Connected through hyperedge HE-2 (induced by source paper "supermix supervising the mixing data augmentation"):
  with members:
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "relational knowledge distillation"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "variational information distillation for knowledge transfer"
    - "fast autoaugment"
    - "contrastive representation distillation"

Connected through hyperedge HE-3 (induced by source paper "progressive local filter pruning for image retrieval acceleration"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "learning sparse sharing architectures for multiple tasks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "fine tuning cnn image retrieval with no human annotation"
    - "channel pruning for accelerating very deep neural networks"
    - "pruning convolutional neural networks for resource efficient inference"

Connected through hyperedge HE-4 (induced by source paper "subclass distillation"):
  with members:
    - "relational knowledge distillation"
    - "momentum contrast for unsupervised visual representation learning"
    - "variational information distillation for knowledge transfer"
    - "contrastive representation distillation"
    - "patient knowledge distillation for bert model compression"
    - "similarity preserving knowledge distillation"
    - "self training with noisy student improves imagenet classification"
    - "large scale distributed neural network training through online distillation"

Connected through hyperedge HE-5 (induced by source paper "feature map level online adversarial knowledge distillation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "federated learning challenges methods and future directions"
    - "knowledge distillation by on the fly native ensemble"
    - "ktan knowledge transfer adversarial network"
    - "deep mutual learning"
    - "towards diverse and natural image descriptions via a conditional gan"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "connecting generative adversarial networks and actor critic methods"

Connected through hyperedge HE-6 (induced by source paper "multi representation knowledge distillation for audio classification"):
  with members:
    - "general audio tagging with ensembling convolutional neural networks and statistical features"
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "weakly supervised crnn system for sound event detection with large scale unlabeled in domain data"
    - "large scale distributed neural network training through online distillation"
    - "sample mixed based data augmentation for domestic audio tagging"
    - "deep mutual learning"
    - "mixup beyond empirical risk minimization"
    - "cooperative learning with visual attributes"

Connected through hyperedge HE-7 (induced by source paper "qkd quantization aware knowledge distillation"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "trained quantization thresholds for accurate and efficient fixed point inference of deep neural networks"
    - "differentiable quantization of deep neural networks"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "a comprehensive overhaul of feature distillation"
    - "feature fusion for online mutual knowledge distillation"
    - "efficient 8 bit quantization of transformer neural machine language translation model"
    - "efficientnet rethinking model scaling for convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "blockswap fisher guided block substitution for network compression on a budget"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "full deep neural network training on a pruned weight budget"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "accelerating deep neural networks with spatial bottleneck modules"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "neural architecture optimization"
    - "darts differentiable architecture search"

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)