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: "a baseline for few shot image classification"
  Abstract: Fine-tuning a deep network trained with the standard cross-entropy loss is a strong baseline for few-shot learning. When fine-tuned transductively, this outperforms the current state-of-the-art on standard datasets such as Mini-Imagenet, Tiered-Imagenet, CIFAR-FS and FC-100 with the same hyper-parameters. The simplicity of this approach enables us to demonstrate the first few-shot learning results on the Imagenet-21k dataset. We find that using a large number of meta-training classes results in high few-shot accuracies even for a large number of test classes. We do not advocate our approach as the solution for few-shot learning, but simply use the results to highlight limitations of current benchmarks and few-shot protocols. We perform extensive studies on benchmark datasets to propose a metric that quantifies the "hardness" of a test episode. This metric can be used to report the performance of few-shot algorithms in a more systematic way.

Connected through hyperedge HE-1 (induced by source paper "optimized generic feature learning for few shot classification across domains"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "large scale adversarial representation learning"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "fast autoaugment"
    - "autodispnet improving disparity estimation with automl"
    - "a closer look at few shot classification"
    - "meta dataset a dataset of datasets for learning to learn from few examples"

Connected through hyperedge HE-2 (induced by source paper "leep a new measure to evaluate transferability of learned representations"):
  with members:
    - "dataset2vec learning dataset meta features"
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "which tasks should be learned together in multi task learning"
    - "p2l predicting transfer learning for images and semantic relations"
    - "transferability and hardness of supervised classification tasks"
    - "task2vec task embedding for meta learning"
    - "fixynn efficient hardware for mobile computer vision via transfer learning"
    - "meta dataset a dataset of datasets for learning to learn from few examples"

Connected through hyperedge HE-3 (induced by source paper "generalized adaptation for few shot learning"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "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-4 (induced by source paper "continual local replacement for few shot learning"):
  with members:
    - "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"
    - "meta transfer learning for few shot learning"

Connected through hyperedge HE-5 (induced by source paper "prototype refinement network for few shot segmentation"):
  with members:
    - "adaptive masked proxies 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"
    - "reptile a scalable metalearning algorithm"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "sg one similarity guidance network for one shot semantic segmentation"
    - "prototypical networks for few shot learning"
    - "one shot learning for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "frustratingly simple few shot object detection"):
  with members:
    - "lvis a dataset for large vocabulary instance segmentation"
    - "tafe net task aware feature embeddings for low shot learning"
    - "a closer look at few shot classification"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "few shot object detection via feature reweighting"
    - "dynamic few shot visual learning without forgetting"
    - "low shot learning with imprinted weights"

Connected through hyperedge HE-7 (induced by source paper "unraveling meta learning understanding feature representations for few shot tasks"):
  with members:
    - "fast and generalized adaptation for few shot learning"
    - "do better imagenet models transfer better"
    - "reptile a scalable metalearning algorithm"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "meta learning with differentiable closed form solvers"
    - "prototypical networks for few shot learning"
    - "visualizing the loss landscape of neural nets"
    - "matching networks for one 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"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "boosting few shot visual learning with self supervision"
    - "revisiting local descriptor based image to class measure 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.LG(Machine Learning)