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: "label propagation for deep semi supervised learning"
  Abstract: Semi-supervised learning is becoming increasingly important because it can combine data carefully labeled by humans with abundant unlabeled data to train deep neural networks. Classic methods on semi-supervised learning that have focused on transductive learning have not been fully exploited in the inductive framework followed by modern deep learning. The same holds for the manifold assumption---that similar examples should get the same prediction. In this work, we employ a transductive label propagation method that is based on the manifold assumption to make predictions on the entire dataset and use these predictions to generate pseudo-labels for the unlabeled data and train a deep neural network. At the core of the transductive method lies a nearest neighbor graph of the dataset that we create based on the embeddings of the same network.Therefore our learning process iterates between these two steps. We improve performance on several datasets especially in the few labels regime and show that our work is complementary to current state of the art.

Connected through hyperedge HE-1 (induced by source paper "two cycle learning clustering based regularisation for deep semi supervised classification"):
  with members:
    - "adversarial robustness may be at odds with simplicity"
    - "semi supervised learning by label gradient alignment"
    - "superpixel contracted graph based learning for hyperspectral image classification"
    - "beyond supervised classification extreme minimal supervision with the graph 1 laplacian"
    - "deep clustering for unsupervised learning of visual features"
    - "deep metric transfer for label propagation with limited annotated data"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "adversarial dropout for supervised and semi supervised learning"

Connected through hyperedge HE-2 (induced by source paper "curriculum labeling self paced pseudo labeling for semi supervised learning"):
  with members:
    - "on the power of curriculum learning in training deep networks"
    - "semi supervised learning by label gradient alignment"
    - "randaugment practical data augmentation with no separate search"
    - "mixmatch a holistic approach to semi supervised learning"
    - "autoaugment learning augmentation policies from data"
    - "averaging weights leads to wider optima and better generalization"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "the extreme value machine"

Connected through hyperedge HE-3 (induced by source paper "end to end graph based deep semi supervised learning"):
  with members:
    - "randaugment practical automated data augmentation with a reduced search space"
    - "remixmatch semi supervised learning with distribution alignment and augmentation anchoring"
    - "mixmatch a holistic approach to semi supervised learning"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised learning via compact latent space clustering"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "good semi supervised learning that requires a bad gan"

Connected through hyperedge HE-4 (induced by source paper "when labelled data hurts deep semi supervised classification with the graph 1 laplacian"):
  with members:
    - "semi supervised learning by label gradient alignment"
    - "weakly supervised deep learning for thoracic disease classification and localization on chest x rays"
    - "comparison of deep learning approaches for multi label chest x ray classification"
    - "dynamic routing on deep neural network for thoracic disease classification and sensitive area localization"
    - "weakly supervised medical diagnosis and localization from multiple resolutions"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "smooth neighbors on teacher graphs for semi supervised learning"
    - "improved techniques for training gans"

Connected through hyperedge HE-5 (induced by source paper "neighborhood based pooling for population level label distribution learning"):
  with members:
    - "deep label distribution learning with label ambiguity"
    - "label distribution learning"

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"
    - "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-7 (induced by source paper "training object detectors from few weakly labeled and many unlabeled images"):
  with members:
    - "weakly supervised object detection with segmentation collaboration"
    - "object aware instance labeling for weakly supervised object detection"
    - "few shot object detection with attention rpn and multi relation detector"
    - "few example object detection with model communication"
    - "feature weighting and boosting for few shot segmentation"
    - "adaptive masked proxies for few shot segmentation"
    - "mixmatch a holistic approach to semi supervised learning"
    - "learning semantic part based models from google images"

Connected through hyperedge HE-8 (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"
    - "attribute aware attention model for fine grained representation learning"
    - "deep adaptive feature embedding with local sample distributions for person re identification"

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)