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: "revisiting local descriptor based image to class measure for few shot learning"
  Abstract: Few-shot learning in image classification aims to learn a classifier to classify images when only few training examples are available for each class. Recent work has achieved promising classification performance, where an image-level feature based measure is usually used. In this paper, we argue that a measure at such a level may not be effective enough in light of the scarcity of examples in few-shot learning. Instead, we think a local descriptor based image-to-class measure should be taken, inspired by its surprising success in the heydays of local invariant features. Specifically, building upon the recent episodic training mechanism, we propose a Deep Nearest Neighbor Neural Network (DN4 in short) and train it in an end-to-end manner. Its key difference from the literature is the replacement of the image-level feature based measure in the final layer by a local descriptor based image-to-class measure. This measure is conducted online via a $k$-nearest neighbor search over the deep local descriptors of convolutional feature maps. The proposed DN4 not only learns the optimal deep local descriptors for the image-to-class measure, but also utilizes the higher efficiency of such a measure in the case of example scarcity, thanks to the exchangeability of visual patterns across the images in the same class. Our work leads to a simple, effective, and computationally efficient framework for few-shot learning. Experimental study on benchmark datasets consistently shows its superiority over the related state-of-the-art, with the largest absolute improvement of $17\%$ over the next best. The source code can be available from \UrlFont{this https URL}.

Connected through hyperedge HE-1 (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"
    - "few shot learning via saliency guided hallucination of samples"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "learning from adversarial features for few shot classification"
    - "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-2 (induced by source paper "few shot few shot learning and the role of spatial attention"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "few shot learning with localization in realistic settings"
    - "finding task relevant features for few shot learning by category traversal"
    - "large scale long tailed recognition in an open world"
    - "a closer look at few shot classification"
    - "few shot learning with metric agnostic conditional embeddings"
    - "deep clustering for unsupervised learning of visual features"
    - "learning embedding adaptation for few shot learning"

Connected through hyperedge HE-3 (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"
    - "boosting few shot visual learning with self supervision"
    - "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-4 (induced by source paper "generalized adaptation for few shot learning"):
  with members:
    - "a baseline for few shot image classification"
    - "meta learning with differentiable convex optimization"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "a closer look at few shot classification"
    - "few shot learning with metric agnostic conditional embeddings"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "meta transfer learning for few shot learning"

Connected through hyperedge HE-5 (induced by source paper "matchinggan matching based few shot image generation"):
  with members:
    - "figr few shot image generation with reptile"
    - "few shot unsupervised image to image translation"
    - "cgans with projection discriminator"
    - "on first order meta learning algorithms"
    - "spectral normalization for generative adversarial networks"
    - "meta transfer learning for few shot learning"
    - "an empirical study on evaluation metrics of generative adversarial networks"
    - "low shot learning from imaginary data"

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 via saliency guided hallucination of samples"
    - "few shot learning with localization in realistic settings"
    - "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 "asymmetric distribution measure for few shot learning"):
  with members:
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "infinite mixture prototypes for few shot learning"
    - "meta learning for semi supervised few shot classification"
    - "delta encoder an effective sample synthesis method for few shot object recognition"
    - "meta transfer learning for few shot learning"
    - "task agnostic meta learning for few shot learning"
    - "began boundary equilibrium generative adversarial networks"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-8 (induced by source paper "associative alignment for few shot image classification"):
  with members:
    - "cross domain few shot classification via learned feature wise transformation"
    - "dense classification and implanting for few shot learning"
    - "variational prototyping encoder one shot learning with prototypical images"
    - "few shot learning via saliency guided hallucination of samples"
    - "a baseline for few shot image classification"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "boosting few shot visual learning with self supervision"

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)