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: "billion scale semi supervised learning for image classification"
  Abstract: This paper presents a study of semi-supervised learning with large convolutional networks. We propose a pipeline, based on a teacher/student paradigm, that leverages a large collection of unlabelled images (up to 1 billion). Our main goal is to improve the performance for a given target architecture, like ResNet-50 or ResNext. We provide an extensive analysis of the success factors of our approach, which leads us to formulate some recommendations to produce high-accuracy models for image classification with semi-supervised learning. As a result, our approach brings important gains to standard architectures for image, video and fine-grained classification. For instance, by leveraging one billion unlabelled images, our learned vanilla ResNet-50 achieves 81.2% top-1 accuracy on the ImageNet benchmark.

Connected through hyperedge HE-1 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation"):
  with members:
    - "monocular 3d human pose estimation by generation and ordinal ranking"
    - "cross view fusion for 3d human pose estimation"
    - "learnable triangulation of human pose"
    - "distill knowledge from nrsfm for weakly supervised 3d pose learning"
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "texturepose supervising human mesh estimation with texture consistency"
    - "in the wild human pose estimation using explicit 2d features and intermediate 3d representations"

Connected through hyperedge HE-2 (induced by source paper "transferring dense pose to proximal animal classes"):
  with members:
    - "semi supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks"
    - "deep learning tools for the measurement of animal behavior in neuroscience"
    - "unsupervised part based disentangling of object shape and appearance"
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"
    - "slim densepose thrifty learning from sparse annotations and motion cues"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "unsupervised learning of landmarks by descriptor vector exchange"
    - "cross domain adaptation for animal pose estimation"

Connected through hyperedge HE-3 (induced by source paper "understanding and improving knowledge distillation"):
  with members:
    - "when does label smoothing help"
    - "revisit knowledge distillation a teacher free framework"
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "bag of tricks for image classification with convolutional neural networks"
    - "knowledge transfer with jacobian matching"
    - "knowledge distillation with adversarial samples supporting decision boundary"
    - "large scale distributed neural network training through online distillation"
    - "heated up softmax embedding"

Connected through hyperedge HE-4 (induced by source paper "noiserank unsupervised label noise reduction with dependence models"):
  with members:
    - "deep self learning from noisy labels"
    - "are anchor points really indispensable in label noise learning"
    - "an effective label noise model for dnn text classification"
    - "unsupervised label noise modeling and loss correction"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "joint optimization framework for learning with noisy labels"
    - "learning to learn from noisy labeled data"

Connected through hyperedge HE-5 (induced by source paper "fixing the train test resolution discrepancy fixefficientnet"):
  with members:
    - "adversarial examples improve image recognition"
    - "fixing the train test resolution discrepancy"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "self training with noisy student improves imagenet classification"
    - "autoaugment learning augmentation policies from data"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "exploring the limits of weakly supervised pretraining"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "self training with noisy student improves imagenet classification"):
  with members:
    - "using videos to evaluate image model robustness"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "semi supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks"
    - "robustness properties of facebook s resnext wsl models"
    - "pseudo labeling and confirmation bias in deep semi supervised learning"
    - "natural adversarial examples"
    - "leveraging just a few keywords for fine grained aspect detection through weakly supervised co training"
    - "randaugment practical automated data augmentation with a reduced search space"

Connected through hyperedge HE-7 (induced by source paper "clusterfit improving generalization of visual representations"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "unsupervised pre training of image features on non curated data"
    - "large scale weakly supervised pre training for video action recognition"
    - "revisiting self supervised visual representation learning"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "large scale distributed neural network training through online distillation"
    - "learning to separate object sounds by watching unlabeled video"

Connected through hyperedge HE-8 (induced by source paper "large scale learning of general visual representations for transfer"):
  with members:
    - "self supervised learning of video induced visual invariances"
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "momentum contrast for unsupervised visual representation learning"
    - "an exponential learning rate schedule for deep learning"
    - "do we train on test data purging cifar of near duplicates"
    - "the visual task adaptation benchmark"
    - "data efficient image recognition with contrastive predictive coding"
    - "remixmatch semi supervised learning with distribution alignment and augmentation anchoring"

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)