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: "few shot learning via saliency guided hallucination of samples"
  Abstract: Learning new concepts from a few of samples is a standard challenge in computer vision. The main directions to improve the learning ability of few-shot training models include (i) a robust similarity learning and (ii) generating or hallucinating additional data from the limited existing samples. In this paper, we follow the latter direction and present a novel data hallucination model. Currently, most datapoint generators contain a specialized network (i.e., GAN) tasked with hallucinating new datapoints, thus requiring large numbers of annotated data for their training in the first place. In this paper, we propose a novel less-costly hallucination method for few-shot learning which utilizes saliency maps. To this end, we employ a saliency network to obtain the foregrounds and backgrounds of available image samples and feed the resulting maps into a two-stream network to hallucinate datapoints directly in the feature space from viable foreground-background combinations. To the best of our knowledge, we are the first to leverage saliency maps for such a task and we demonstrate their usefulness in hallucinating additional datapoints for few-shot learning. Our proposed network achieves the state of the art on publicly available datasets.

Connected through hyperedge HE-1 (induced by source paper "incremental few shot object detection"):
  with members:
    - "boosting few shot visual learning with self supervision"
    - "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-2 (induced by source paper "deepemd few shot image classification with differentiable earth mover s distance and structured classifiers"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "learning from adversarial features for few shot classification"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "meta learning with warped gradient descent"
    - "edge labeling graph neural network for few shot learning"
    - "finding task relevant features for few shot learning by category traversal"

Connected through hyperedge HE-3 (induced by source paper "hallucinating statistical moment and subspace descriptors from object and saliency detectors for action recognition"):
  with members:
    - "hallucinating optical flow features for video classification"
    - "assemblenet searching for multi stream neural connectivity in video architectures"
    - "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns"
    - "deeply supervised salient object detection with short connections"
    - "learning discriminative video representations using adversarial perturbations"
    - "evolving space time neural architectures for videos"
    - "a deeper look at power normalizations"
    - "non linear temporal subspace representations for activity recognition"

Connected through hyperedge HE-4 (induced by source paper "exploiting unsupervised inputs for accurate few shot classification"):
  with members:
    - "a two stage approach to few shot learning for image recognition"
    - "simpleshot revisiting nearest neighbor classification for few shot learning"
    - "meta learning with differentiable convex optimization"
    - "generalizing from a few examples a survey on few shot learning"
    - "prototype rectification for few shot learning"
    - "charting the right manifold manifold mixup for few shot learning"
    - "edge labeling graph neural network for few shot learning"
    - "finding task relevant features for few shot learning by category traversal"

Connected through hyperedge HE-5 (induced by source paper "few shot action recognition via improved attention with self supervision"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "boosting few shot visual learning with self supervision"
    - "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-6 (induced by source paper "few shot learning with weakly supervised object localization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "baby steps towards few shot learning with multiple semantics"
    - "few shot learning for domain specfic fine grained image classfication"
    - "few shot learning with localization in realistic settings"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "laso label set operations networks for multi label few shot learning"
    - "do imagenet classifiers generalize to imagenet"
    - "revisiting metric learning for few shot image classification"

Connected through hyperedge HE-7 (induced by source paper "few shot learning with multi scale self supervision"):
  with members:
    - "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"
    - "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns"
    - "meta learning with latent embedding optimization"
    - "how to train your maml"
    - "tadam task dependent adaptive metric for improved few shot learning"

Connected through hyperedge HE-8 (induced by source paper "transferring cross domain knowledge for video sign language recognition"):
  with members:
    - "word level deep sign language recognition from video a new large scale dataset and methods comparison"
    - "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns"
    - "ms asl a large scale data set and benchmark for understanding american sign language"
    - "neural sign language translation based on human keypoint estimation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "prototypical networks for few shot learning"
    - "attend to you personalized image captioning with context sequence memory networks"
    - "squeeze and excitation networks"

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)