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 for semi supervised few shot classification"
  Abstract: In few-shot classification, we are interested in learning algorithms that train a classifier from only a handful of labeled examples. Recent progress in few-shot classification has featured meta-learning, in which a parameterized model for a learning algorithm is defined and trained on episodes representing different classification problems, each with a small labeled training set and its corresponding test set. In this work, we advance this few-shot classification paradigm towards a scenario where unlabeled examples are also available within each episode. We consider two situations: one where all unlabeled examples are assumed to belong to the same set of classes as the labeled examples of the episode, as well as the more challenging situation where examples from other distractor classes are also provided. To address this paradigm, we propose novel extensions of Prototypical Networks (Snell et al., 2017) that are augmented with the ability to use unlabeled examples when producing prototypes. These models are trained in an end-to-end way on episodes, to learn to leverage the unlabeled examples successfully. We evaluate these methods on versions of the Omniglot and miniImageNet benchmarks, adapted to this new framework augmented with unlabeled examples. We also propose a new split of ImageNet, consisting of a large set of classes, with a hierarchical structure. Our experiments confirm that our Prototypical Networks can learn to improve their predictions due to unlabeled examples, much like a semi-supervised algorithm would.

Connected through hyperedge HE-1 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning"):
  with members:
    - "learning to continually learn"
    - "fine grained continual learning"
    - "continuous meta learning without tasks"
    - "reinforcement learning in non stationary environments"
    - "online learned continual compression with stacked quantization modules"
    - "continual lifelong learning with neural networks a review"
    - "online meta learning"
    - "regularization shortcomings for continual learning"

Connected through hyperedge HE-2 (induced by source paper "a new meta baseline for few shot learning"):
  with members:
    - "tafe net task aware feature embeddings for low shot learning"
    - "a closer look at few shot classification"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"
    - "dropblock a regularization method for convolutional networks"
    - "meta transfer learning for few shot learning"
    - "dynamic few shot visual learning without forgetting"
    - "low shot learning with imprinted weights"

Connected through hyperedge HE-3 (induced by source paper "asymmetric distribution measure for few shot learning"):
  with members:
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "infinite mixture prototypes for few shot learning"
    - "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-4 (induced by source paper "a robot that learns connect four using game theory and demonstrations"):
  with members:
    - "few shot learning with meta metric learners"
    - "are few shot learning benchmarks too simple"
    - "centroid networks for few shot clustering and unsupervised few shot classification"
    - "one shot hierarchical imitation learning of compound visuomotor tasks"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "learning game representations from data using rationality constraints"

Connected through hyperedge HE-5 (induced by source paper "boosting few shot visual learning with self supervision"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "revisiting self supervised visual representation learning"
    - "meta learning with latent embedding optimization"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "unsupervised learning via meta learning"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-6 (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-7 (induced by source paper "a baseline for few shot image classification"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "learning internal representations"
    - "a closer look at few shot classification"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "few shot learning with embedded class models and shot free meta training"
    - "on first order meta learning algorithms"
    - "bag of tricks for image classification with convolutional neural networks"
    - "meta learning with latent embedding optimization"

Connected through hyperedge HE-8 (induced by source paper "generating classification weights with gnn denoising autoencoders for few shot learning"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "meta learning with latent embedding optimization"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "rethinking knowledge graph propagation for zero shot learning"
    - "meta learning with differentiable closed form solvers"
    - "low shot learning from imaginary data"
    - "dynamic few shot visual learning without forgetting"

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)