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: "infinite mixture prototypes for few shot learning"
  Abstract: We propose infinite mixture prototypes to adaptively represent both simple and complex data distributions for few-shot learning. Our infinite mixture prototypes represent each class by a set of clusters, unlike existing prototypical methods that represent each class by a single cluster. By inferring the number of clusters, infinite mixture prototypes interpolate between nearest neighbor and prototypical representations, which improves accuracy and robustness in the few-shot regime. We show the importance of adaptive capacity for capturing complex data distributions such as alphabets, with 25% absolute accuracy improvements over prototypical networks, while still maintaining or improving accuracy on the standard Omniglot and mini-ImageNet benchmarks. In clustering labeled and unlabeled data by the same clustering rule, infinite mixture prototypes achieves state-of-the-art semi-supervised accuracy. As a further capability, we show that infinite mixture prototypes can perform purely unsupervised clustering, unlike existing prototypical methods.

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"
    - "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"
    - "few shot learning with embedded class models and shot free meta training"

Connected through hyperedge HE-2 (induced by source paper "meta learning across meta tasks for few shot learning"):
  with members:
    - "cross domain few shot classification via learned feature wise transformation"
    - "learning what and where to transfer"
    - "charting the right manifold manifold mixup for few shot learning"
    - "on minimum discrepancy estimation for deep domain adaptation"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "bridging theory and algorithm for domain adaptation"
    - "transductive episodic wise adaptive metric for few shot learning"
    - "a closer look at few shot classification"

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"
    - "meta learning for semi supervised few shot classification"
    - "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 "task adaptive clustering for semi supervised few shot classification"):
  with members:
    - "lgm net learning to generate matching networks for few shot learning"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "a simple neural attentive meta learner"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-5 (induced by source paper "meta learning with implicit gradients"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "a differentiable augmented lagrangian method for bilevel nonlinear optimization"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "on first order meta learning algorithms"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"
    - "modular meta learning"

Connected through hyperedge HE-6 (induced by source paper "prototype rectification for few shot learning"):
  with members:
    - "a baseline for few shot image classification"
    - "meta learning with differentiable convex optimization"
    - "boosting few shot visual learning with self supervision"
    - "edge labeling graph neural network for few shot learning"
    - "a closer look at few shot classification"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "learning to propagate labels transductive propagation network for few shot learning"

Connected through hyperedge HE-7 (induced by source paper "hyperspherical prototype networks"):
  with members:
    - "prototypical metric transfer learning for continuous speech keyword spotting with limited training data"
    - "transferrable prototypical networks for unsupervised domain adaptation"
    - "deep learning on small datasets without pre training using cosine loss"
    - "decoupled networks"
    - "fix your classifier the marginal value of training the last weight layer"
    - "cosface large margin cosine loss for deep face recognition"
    - "robust classification with convolutional prototype learning"
    - "learning towards minimum hyperspherical energy"

Connected through hyperedge HE-8 (induced by source paper "continuous meta learning without tasks"):
  with members:
    - "meta learning representations for continual learning"
    - "introduction to online convex optimization"
    - "a closer look at few shot classification"
    - "task free continual learning"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "online gradient based mixtures for transfer modulation in meta learning"
    - "meta learning priors for efficient online bayesian regression"
    - "deep online learning via meta learning continual adaptation for model based rl"

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)