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: "image deformation meta networks for one shot learning"
  Abstract: Humans can robustly learn novel visual concepts even when images undergo various deformations and lose certain information. Mimicking the same behavior and synthesizing deformed instances of new concepts may help visual recognition systems perform better one-shot learning, i.e., learning concepts from one or few examples. Our key insight is that, while the deformed images may not be visually realistic, they still maintain critical semantic information and contribute significantly to formulating classifier decision boundaries. Inspired by the recent progress of meta-learning, we combine a meta-learner with an image deformation sub-network that produces additional training examples, and optimize both models in an end-to-end manner. The deformation sub-network learns to deform images by fusing a pair of images --- a probe image that keeps the visual content and a gallery image that diversifies the deformations. We demonstrate results on the widely used one-shot learning benchmarks (miniImageNet and ImageNet 1K Challenge datasets), which significantly outperform state-of-the-art approaches. Code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "incremental few shot object detection"):
  with members:
    - "few shot learning via saliency guided hallucination of samples"
    - "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"

Connected through hyperedge HE-2 (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"
    - "few shot learning via saliency guided hallucination of samples"
    - "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"

Connected through hyperedge HE-3 (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"
    - "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"
    - "meta transfer learning for few shot learning"

Connected through hyperedge HE-4 (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"

Connected through hyperedge HE-5 (induced by source paper "revisiting metric learning for few shot image classification"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "multi level semantic feature augmentation for one shot learning"
    - "meta learning with differentiable convex optimization"
    - "lgm net learning to generate matching networks for few shot learning"
    - "few shot learning with localization in realistic settings"
    - "revisiting local descriptor based image to class measure 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-6 (induced by source paper "few shot learning with contextual cueing for object recognition in complex scenes"):
  with members:
    - "baby steps towards few shot learning with multiple semantics"
    - "dense classification and implanting for few shot learning"
    - "context aware zero shot learning for object recognition"
    - "few shot learning via saliency guided hallucination of samples"
    - "few shot learning with localization in realistic settings"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "tafe net task aware feature embeddings for low shot learning"
    - "adaptive cross modal few shot learning"

Connected through hyperedge HE-7 (induced by source paper "finding task relevant features for few shot learning by category traversal"):
  with members:
    - "a closer look at few shot classification"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "neural network encapsulation"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "incremental few shot learning with attention attractor networks"
    - "improving generalization via scalable neighborhood component analysis"
    - "dynamic few shot visual learning without forgetting"

Connected through hyperedge HE-8 (induced by source paper "meta reinforced synthetic data for one shot fine grained visual recognition"):
  with members:
    - "image generation from small datasets via batch statistics adaptation"
    - "how good is my gan"
    - "delta encoder an effective sample synthesis method for few shot object recognition"
    - "spectral normalization for generative adversarial networks"
    - "low shot learning from imaginary data"
    - "probabilistic model agnostic meta learning"
    - "modulating early visual processing by language"
    - "prototypical networks 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)