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 metric transfer for label propagation with limited annotated data"
  Abstract: We study object recognition under the constraint that each object class is only represented by very few observations. Semi-supervised learning, transfer learning, and few-shot recognition all concern with achieving fast generalization with few labeled data. In this paper, we propose a generic framework that utilizes unlabeled data to aid generalization for all three tasks. Our approach is to create much more training data through label propagation from the few labeled examples to a vast collection of unannotated images. The main contribution of the paper is that we show such a label propagation scheme can be highly effective when the similarity metric used for propagation is transferred from other related domains. We test various combinations of supervised and unsupervised metric learning methods with various label propagation algorithms. We find that our framework is very generic without being sensitive to any specific techniques. By taking advantage of unlabeled data in this way, we achieve significant improvements on all three tasks.

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"
    - "label propagation for deep semi supervised learning"
    - "deep clustering for unsupervised learning of visual features"
    - "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 "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-3 (induced by source paper "s4l self supervised semi supervised learning"):
  with members:
    - "data efficient image recognition with contrastive predictive coding"
    - "unsupervised data augmentation for consistency training"
    - "mixmatch a holistic approach to semi supervised learning"
    - "revisiting self supervised visual representation learning"
    - "grasp2vec learning object representations from self supervised grasping"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-4 (induced by source paper "local label propagation for large scale semi supervised learning"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "billion scale semi supervised learning for image classification"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "label propagation for deep semi supervised learning"
    - "deep clustering for unsupervised learning of visual features"
    - "deep co training for semi supervised image recognition"
    - "improving generalization via scalable neighborhood component analysis"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-5 (induced by source paper "mixmatch a holistic approach to semi supervised learning"):
  with members:
    - "autoaugment learning augmentation policies from data"
    - "three mechanisms of weight decay regularization"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "invariant information clustering for unsupervised image classification and segmentation"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "decoupled weight decay regularization"
    - "mixup beyond empirical risk minimization"
    - "semi supervised learning with context conditional generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "remixmatch semi supervised learning with distribution alignment and augmentation anchoring"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "unsupervised data augmentation for consistency training"
    - "mixmatch a holistic approach to semi supervised learning"
    - "autoaugment learning augmentation policies from data"
    - "unsupervised representation learning by predicting image rotations"
    - "three mechanisms of weight decay regularization"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

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)