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: "meta learning with differentiable closed form solvers"
  Abstract: Adapting deep networks to new concepts from a few examples is challenging, due to the high computational requirements of standard fine-tuning procedures. Most work on few-shot learning has thus focused on simple learning techniques for adaptation, such as nearest neighbours or gradient descent. Nonetheless, the machine learning literature contains a wealth of methods that learn non-deep models very efficiently. In this paper, we propose to use these fast convergent methods as the main adaptation mechanism for few-shot learning. The main idea is to teach a deep network to use standard machine learning tools, such as ridge regression, as part of its own internal model, enabling it to quickly adapt to novel data. This requires back-propagating errors through the solver steps. While normally the cost of the matrix operations involved in such a process would be significant, by using the Woodbury identity we can make the small number of examples work to our advantage. We propose both closed-form and iterative solvers, based on ridge regression and logistic regression components. Our methods constitute a simple and novel approach to the problem of few-shot learning and achieve performance competitive with or superior to the state of the art on three benchmarks.

Connected through hyperedge HE-1 (induced by source paper "adargcn adaptive aggregation gcn for few shot learning"):
  with members:
    - "dual graph convolutional network for semantic segmentation"
    - "meta learning with differentiable convex optimization"
    - "infinite mixture prototypes for few shot learning"
    - "t gcn a temporal graph convolutional network for traffic prediction"
    - "edge labeling graph neural network for few shot learning"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "semantic graph convolutional networks for 3d human pose regression"
    - "a closer look at few shot classification"

Connected through hyperedge HE-2 (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-3 (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-4 (induced by source paper "starnet towards weakly supervised few shot detection and explainable few shot classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "dense classification and implanting for few shot learning"
    - "generative modeling for small data object detection"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "finding task relevant features for few shot learning by category traversal"
    - "laso label set operations networks for multi label few shot learning"
    - "learning to find common objects across few image collections"

Connected through hyperedge HE-5 (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-6 (induced by source paper "meta learning for mixed linear regression"):
  with members:
    - "learning mixtures of linear regressions in subexponential time via fourier moments"
    - "sublinear optimal policy value estimation in contextual bandits"
    - "meta learning with implicit gradients"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "estimating learnability in the sublinear data regime"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "learning mixtures of linear regressions with nearly optimal complexity"
    - "meta learning with latent embedding optimization"

Connected through hyperedge HE-7 (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"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "meta3d single view 3d object reconstruction from shape priors in memory"):
  with members:
    - "pix2vox context aware 3d reconstruction from single and multi view images"
    - "adaptive semantic visual tree for hierarchical embeddings"
    - "meta learning with latent embedding optimization"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "domain adaptive single view 3d reconstruction"
    - "probabilistic model agnostic meta learning"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "learning shape priors for single view 3d completion and reconstruction"

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)