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: "bag of tricks for image classification with convolutional neural networks"
  Abstract: Much of the recent progress made in image classification research can be credited to training procedure refinements, such as changes in data augmentations and optimization methods. In the literature, however, most refinements are either briefly mentioned as implementation details or only visible in source code. In this paper, we will examine a collection of such refinements and empirically evaluate their impact on the final model accuracy through ablation study. We will show that, by combining these refinements together, we are able to improve various CNN models significantly. For example, we raise ResNet-50's top-1 validation accuracy from 75.3% to 79.29% on ImageNet. We will also demonstrate that improvement on image classification accuracy leads to better transfer learning performance in other application domains such as object detection and semantic segmentation.

Connected through hyperedge HE-1 (induced by source paper "attention guided chained context aggregation for semantic segmentation"):
  with members:
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "selective kernel networks"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "deep high resolution representation learning for human pose estimation"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"
    - "ccnet criss cross attention for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "rethinking image mixture for unsupervised visual representation learning"):
  with members:
    - "a simple framework for contrastive learning of visual representations"
    - "improved baselines with momentum contrastive learning"
    - "contrastive multiview coding"
    - "when does label smoothing help"
    - "momentum contrast for unsupervised visual representation learning"
    - "large scale adversarial representation learning"
    - "unsupervised pre training of image features on non curated data"
    - "bag of freebies for training object detection neural networks"

Connected through hyperedge HE-3 (induced by source paper "rethinking the hyperparameters for fine tuning"):
  with members:
    - "task2vec task embedding for meta learning"
    - "rethinking imagenet pre training"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "do better imagenet models transfer better"
    - "toward deeper understanding of nonconvex stochastic optimization with momentum using diffusion approximations"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "domain adaptive transfer learning with specialist models"
    - "autoaugment learning augmentation policies from data"

Connected through hyperedge HE-4 (induced by source paper "xsepconv extremely separated convolution"):
  with members:
    - "convolution with even sized kernels and symmetric padding"
    - "mixconv mixed depthwise convolutional kernels"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-5 (induced by source paper "compounding the performance improvements of assembled techniques in a convolutional neural network"):
  with members:
    - "selective kernel networks"
    - "adversarial examples improve image recognition"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "do better imagenet models transfer better"
    - "batch dropblock network for person re identification and beyond"
    - "dropblock a regularization method for convolutional networks"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-6 (induced by source paper "an ensemble of epoch wise empirical bayes for few shot learning"):
  with members:
    - "multi level semantic feature augmentation for one shot learning"
    - "meta learning with differentiable convex optimization"
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "lgm net learning to generate matching networks for few shot learning"
    - "finding task relevant features for few shot learning by category traversal"
    - "meta transfer learning through hard tasks"
    - "a closer look at few shot classification"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"

Connected through hyperedge HE-7 (induced by source paper "fastai a layered api for deep learning"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "developing an app to interpret chest x rays to support the diagnosis of respiratory pathology with artificial intelligence"
    - "suggestion mining from online reviews using ulmfit"
    - "multiclass language identification using deep learning on spectral images of audio signals"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "mish a self regularized non monotonic neural activation function"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"

Connected through hyperedge HE-8 (induced by source paper "natural image matting via guided contextual attention"):
  with members:
    - "inductive guided filter real time deep image matting with weakly annotated masks on mobile devices"
    - "indices matter learning to index for deep image matting"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "alphagan generative adversarial networks for natural image matting"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "generative image inpainting with contextual attention"

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)