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: "boosting few shot visual learning with self supervision"
  Abstract: Few-shot learning and self-supervised learning address different facets of the same problem: how to train a model with little or no labeled data. Few-shot learning aims for optimization methods and models that can learn efficiently to recognize patterns in the low data regime. Self-supervised learning focuses instead on unlabeled data and looks into it for the supervisory signal to feed high capacity deep neural networks. In this work we exploit the complementarity of these two domains and propose an approach for improving few-shot learning through self-supervision. We use self-supervision as an auxiliary task in a few-shot learning pipeline, enabling feature extractors to learn richer and more transferable visual representations while still using few annotated samples. Through self-supervision, our approach can be naturally extended towards using diverse unlabeled data from other datasets in the few-shot setting. We report consistent improvements across an array of architectures, datasets and self-supervision techniques.

Connected through hyperedge HE-1 (induced by source paper "learning representations by predicting bags of visual words"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "s4l self supervised semi supervised learning"
    - "when does self supervision improve few shot learning"
    - "contrastive multiview coding"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "momentum contrast for unsupervised visual representation learning"
    - "using self supervised learning can improve model robustness and uncertainty"

Connected through hyperedge HE-2 (induced by source paper "incremental few shot object detection"):
  with members:
    - "few shot learning via saliency guided hallucination of samples"
    - "large scale long tailed recognition in an open world"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "zero shot detection"
    - "a closer look at few shot classification"
    - "continual learning a comparative study on how to defy forgetting in classification tasks"
    - "objects as points"
    - "image deformation meta networks for one shot learning"

Connected through hyperedge HE-3 (induced by source paper "tafssl task adaptive feature sub space learning for few shot classification"):
  with members:
    - "baby steps towards few shot learning with multiple semantics"
    - "multi level semantic feature augmentation for one shot learning"
    - "simpleshot revisiting nearest neighbor classification for few shot learning"
    - "meta learning with differentiable convex optimization"
    - "edge labeling graph neural network for few shot learning"
    - "finding task relevant features for few shot learning by category traversal"
    - "laso label set operations networks for multi label few shot learning"
    - "fast autoaugment"

Connected through hyperedge HE-4 (induced by source paper "learning to compare relation semantic alignment for few shot learning"):
  with members:
    - "a two stage approach to few shot learning for image recognition"
    - "dense classification and implanting for few shot learning"
    - "learning video representations using contrastive bidirectional transformer"
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "few shot learning with localization in realistic settings"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "adaptive posterior learning few shot learning with a surprise based memory module"
    - "compare more nuanced pairwise alignment bilinear network for few shot fine grained learning"

Connected through hyperedge HE-5 (induced by source paper "embedding propagation smoother manifold for few shot classification"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "charting the right manifold manifold mixup for few shot learning"
    - "meta learning with implicit gradients"
    - "a closer look at few shot classification"
    - "adaptive cross modal few shot learning"
    - "few shot learning with metric agnostic conditional embeddings"
    - "reptile a scalable metalearning algorithm"
    - "meta learning with latent embedding optimization"

Connected through hyperedge HE-6 (induced by source paper "few shot action recognition via improved attention with self supervision"):
  with members:
    - "few shot learning via saliency guided hallucination of samples"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "edge labeling graph neural network for few shot learning"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "boosting supervision with self supervision for few shot learning"
    - "moments in time dataset one million videos for event understanding"
    - "how to train your maml"

Connected through hyperedge HE-7 (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"
    - "semi supervised learning with scarce annotations"
    - "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-8 (induced by source paper "semi supervised few shot learning for medical image segmentation"):
  with members:
    - "on the texture bias for few shot cnn segmentation"
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "fss 1000 a 1000 class dataset for few shot segmentation"
    - "feature weighting and boosting for few shot segmentation"
    - "panet few shot image semantic segmentation with prototype alignment"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"

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)