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: "low shot learning with imprinted weights"
  Abstract: Human vision is able to immediately recognize novel visual categories after seeing just one or a few training examples. We describe how to add a similar capability to ConvNet classifiers by directly setting the final layer weights from novel training examples during low-shot learning. We call this process weight imprinting as it directly sets weights for a new category based on an appropriately scaled copy of the embedding layer activations for that training example. The imprinting process provides a valuable complement to training with stochastic gradient descent, as it provides immediate good classification performance and an initialization for any further fine-tuning in the future. We show how this imprinting process is related to proxy-based embeddings. However, it differs in that only a single imprinted weight vector is learned for each novel category, rather than relying on a nearest-neighbor distance to training instances as typically used with embedding methods. Our experiments show that using averaging of imprinted weights provides better generalization than using nearest-neighbor instance embeddings.

Connected through hyperedge HE-1 (induced by source paper "cafenet class agnostic few shot edge detection network"):
  with members:
    - "transductive few shot learning with meta learned confidence"
    - "dense classification and implanting for few shot learning"
    - "bi directional cascade network for perceptual edge detection"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "fss 1000 a 1000 class dataset for few shot segmentation"
    - "tapnet neural network augmented with task adaptive projection for few shot learning"
    - "dynamic feature fusion for semantic edge detection"
    - "panet few shot image semantic segmentation with prototype alignment"

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"
    - "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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "a two stage approach to few shot learning for image recognition"):
  with members:
    - "generalizing from a few examples a survey on few shot learning"
    - "adaptive posterior learning few shot learning with a surprise based memory module"
    - "few shot learning with metric agnostic conditional embeddings"
    - "on first order meta learning algorithms"
    - "sample to sample correspondence for unsupervised domain adaptation"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "unsupervised domain adaptation using regularized hyper graph matching"
    - "meta transfer learning for few shot learning"

Connected through hyperedge HE-4 (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"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "any shot object detection"):
  with members:
    - "anchor loss modulating loss scale based on prediction difficulty"
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "few example object detection with model communication"
    - "zero shot detection"
    - "a closer look at few shot classification"
    - "domain invariant projection learning for zero shot recognition"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "zero shot object detection by hybrid region embedding"

Connected through hyperedge HE-6 (induced by source paper "continual local replacement for few shot learning"):
  with members:
    - "a baseline for few shot image classification"
    - "meta learning with differentiable convex optimization"
    - "a closer look at few shot classification"
    - "image deformation meta networks for one shot learning"
    - "do better imagenet models transfer better"
    - "meta learning with latent embedding optimization"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "deep meta learning learning to learn in the concept space"

Connected through hyperedge HE-7 (induced by source paper "learning meta face recognition in unseen domains"):
  with members:
    - "meta learning with implicit gradients"
    - "attentional feature pair relation networks for accurate face recognition"
    - "on first order meta learning algorithms"
    - "orthogonal deep features decomposition for age invariant face recognition"
    - "face synthesis for eyeglass robust face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "meta transfer learning for few shot learning"
    - "dynamic few shot visual learning without forgetting"

Connected through hyperedge HE-8 (induced by source paper "transductive few shot learning with meta learned confidence"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "edge labeling graph neural network for few shot learning"
    - "tapnet neural network augmented with task adaptive projection for few shot learning"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"
    - "fast context adaptation via meta learning"
    - "learning to propagate labels transductive propagation network for few shot learning"

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)