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: "semi supervised learning with scarce annotations"
  Abstract: While semi-supervised learning (SSL) algorithms provide an efficient way to make use of both labelled and unlabelled data, they generally struggle when the number of annotated samples is very small. In this work, we consider the problem of SSL multi-class classification with very few labelled instances. We introduce two key ideas. The first is a simple but effective one: we leverage the power of transfer learning among different tasks and self-supervision to initialize a good representation of the data without making use of any label. The second idea is a new algorithm for SSL that can exploit well such a pre-trained representation. #R##N#The algorithm works by alternating two phases, one fitting the labelled points and one fitting the unlabelled ones, with carefully-controlled information flow between them. The benefits are greatly reducing overfitting of the labelled data and avoiding issue with balancing labelled and unlabelled losses during training. We show empirically that this method can successfully train competitive models with as few as 10 labelled data points per class. More in general, we show that the idea of bootstrapping features using self-supervised learning always improves SSL on standard benchmarks. We show that our algorithm works increasingly well compared to other methods when refining from other tasks or datasets.

Connected through hyperedge HE-1 (induced by source paper "automatically discovering and learning new visual categories with ranking statistics"):
  with members:
    - "learning to discover novel visual categories via deep transfer clustering"
    - "multi class classification without multi class labels"
    - "revisiting self supervised visual representation learning"
    - "a survey on deep transfer learning"
    - "unsupervised representation learning by predicting image rotations"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "memory aware synapses learning what not to forget"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-2 (induced by source paper "self supervised contextual bandits in computer vision"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "exploiting unlabeled data in cnns by self supervised learning to rank"
    - "self supervised exploration via disagreement"
    - "boosting few shot visual learning with self supervision"
    - "deep clustering for unsupervised learning of visual features"
    - "two stream self supervised learning for action recognition"
    - "large scale study of curiosity driven learning"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-3 (induced by source paper "pseudo labeling and confirmation bias in deep semi supervised learning"):
  with members:
    - "exploiting unlabeled data in cnns by self supervised learning to rank"
    - "surprising effectiveness of few image unsupervised feature learning"
    - "unsupervised label noise modeling and loss correction"
    - "label propagation for deep semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "revisiting self supervised visual representation learning"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "deep visual domain adaptation a survey"

Connected through hyperedge HE-4 (induced by source paper "are labels required for improving adversarial robustness"):
  with members:
    - "an alternative surrogate loss for pgd based adversarial testing"
    - "adversarially robust generalization just requires more unlabeled data"
    - "theoretically principled trade off between robustness and accuracy"
    - "unsupervised data augmentation for consistency training"
    - "using pre training can improve model robustness and uncertainty"
    - "mixmatch a holistic approach to semi supervised learning"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"

Connected through hyperedge HE-5 (induced by source paper "rethinking deep active learning using unlabeled data at model training"):
  with members:
    - "variational adversarial active learning"
    - "label propagation for deep semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "adversarial active learning for deep networks a margin based approach"
    - "deep clustering for unsupervised learning of visual features"
    - "revisiting pre training an efficient training method for image classification"
    - "large scale visual active learning with deep probabilistic ensembles"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-6 (induced by source paper "learning to discover novel visual categories via deep transfer clustering"):
  with members:
    - "multi class classification without multi class labels"
    - "are few shot learning benchmarks too simple"
    - "centroid networks for few shot clustering and unsupervised few shot classification"
    - "deep visual domain adaptation a survey"
    - "a survey on deep transfer learning"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "prototypical networks for few shot learning"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"

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)