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: "deep co training for semi supervised image recognition"
  Abstract: In this paper, we study the problem of semi-supervised image recognition, which is to learn classifiers using both labeled and unlabeled images. We present Deep Co-Training, a deep learning based method inspired by the Co-Training framework. The original Co-Training learns two classifiers on two views which are data from different sources that describe the same instances. To extend this concept to deep learning, Deep Co-Training trains multiple deep neural networks to be the different views and exploits adversarial examples to encourage view difference, in order to prevent the networks from collapsing into each other. As a result, the co-trained networks provide different and complementary information about the data, which is necessary for the Co-Training framework to achieve good results. We test our method on SVHN, CIFAR-10/100 and ImageNet datasets, and our method outperforms the previous state-of-the-art methods by a large margin.

Connected through hyperedge HE-1 (induced by source paper "proposal learning for semi supervised object detection"):
  with members:
    - "momentum contrast for unsupervised visual representation learning"
    - "deep high resolution representation learning for visual recognition"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "mask scoring r cnn"
    - "benchmarking robustness in object detection autonomous driving when winter is coming"
    - "unsupervised data augmentation for consistency training"
    - "revisiting self supervised visual representation learning"

Connected through hyperedge HE-2 (induced by source paper "flame a self adaptive auto labeling system for heterogeneous mobile processors"):
  with members:
    - "cross modal data programming enables rapid medical machine learning"
    - "federated learning strategies for improving communication efficiency"
    - "runtime concurrency control and operation scheduling for high performance neural network training"
    - "deeprebirth accelerating deep neural network execution on mobile devices"
    - "inferring generative model structure with static analysis"
    - "snorkel rapid training data creation with weak supervision"
    - "morphnet fast simple resource constrained structure learning of deep networks"
    - "learning the structure of generative models without labeled data"

Connected through hyperedge HE-3 (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"
    - "billion scale semi supervised learning for image classification"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "progressive cross camera soft label learning for semi supervised person re identification"):
  with members:
    - "camera adversarial transfer for unsupervised person re identification"
    - "imitating targets from all sides an unsupervised transfer learning method for person re identification"
    - "unsupervised tracklet person re identification"
    - "a novel unsupervised camera aware domain adaptation framework for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "label propagation for deep semi supervised learning"
    - "attribute aware attention model for fine grained representation learning"

Connected through hyperedge HE-5 (induced by source paper "d textbf s 3 l deep self semi supervised learning for image recognition"):
  with members:
    - "small data challenges in big data era a survey of recent progress on unsupervised and semi supervised methods"
    - "s4l self supervised semi supervised learning"
    - "a meta approach to defend noisy labels by the manifold regularizer psdr"
    - "unsupervised label noise modeling and loss correction"
    - "pushing the right boundaries matters wasserstein adversarial training for label noise"
    - "weight standardization"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "revisiting self supervised visual representation learning"

Connected through hyperedge HE-6 (induced by source paper "deeply supervised knowledge synergy"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "knowledge transfer with jacobian matching"
    - "dropblock a regularization method for convolutional networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "deep supervision with intermediate concepts"
    - "deeptravel a neural network based travel time estimation model with auxiliary supervision"
    - "modality distillation with multiple stream networks for action recognition"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-7 (induced by source paper "deep co training for semi supervised image segmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "constrained cnn losses for weakly supervised segmentation"
    - "deeply supervised salient object detection with short connections"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "a two stream mutual attention network for semi supervised biomedical segmentation with noisy labels"
    - "adversarial learning for semi supervised semantic segmentation"
    - "realistic evaluation of deep semi supervised learning algorithms"

Connected through hyperedge HE-8 (induced by source paper "countering noisy labels by learning from auxiliary clean labels"):
  with members:
    - "a meta approach to defend noisy labels by the manifold regularizer psdr"
    - "unsupervised label noise modeling and loss correction"
    - "pushing the right boundaries matters wasserstein adversarial training for label noise"
    - "weight standardization"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "revisiting self supervised visual representation learning"
    - "high fidelity image generation with fewer labels"
    - "group normalization"

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)