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: "bayesian model agnostic meta learning"
  Abstract: Learning to infer Bayesian posterior from a few-shot dataset is an important step towards robust meta-learning due to the model uncertainty inherent in the problem. In this paper, we propose a novel Bayesian model-agnostic meta-learning method. The proposed method combines scalable gradient-based meta-learning with nonparametric variational inference in a principled probabilistic framework. During fast adaptation, the method is capable of learning complex uncertainty structure beyond a point estimate or a simple Gaussian approximation. In addition, a robust Bayesian meta-update mechanism with a new meta-loss prevents overfitting during meta-update. Remaining an efficient gradient-based meta-learner, the method is also model-agnostic and simple to implement. Experiment results show the accuracy and robustness of the proposed method in various tasks: sinusoidal regression, image classification, active learning, and reinforcement learning.

Connected through hyperedge HE-1 (induced by source paper "is the meta learning idea able to improve the generalization of deep neural networks on the standard supervised learning"):
  with members:
    - "alpha maml adaptive model agnostic meta learning"
    - "on first order meta learning algorithms"
    - "training behavior of deep neural network in frequency domain"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "how to train your maml"
    - "understanding training and generalization in deep learning by fourier analysis"
    - "auto meta automated gradient based meta learner search"
    - "online learning rate adaptation with hypergradient descent"

Connected through hyperedge HE-2 (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-3 (induced by source paper "local nonparametric meta learning"):
  with members:
    - "adaptive deep kernel learning"
    - "meta learning without memorization"
    - "towards understanding generalization in gradient based meta learning"
    - "attentive neural processes"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "fast context adaptation via meta learning"
    - "conditional neural processes"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "meta reinforcement learning with autonomous inference of subtask dependencies"):
  with members:
    - "neural logic machines"
    - "on first order meta learning algorithms"
    - "hierarchical reinforcement learning for zero shot generalization with subtask dependencies"
    - "meta reinforcement learning of structured exploration strategies"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "probabilistic model agnostic meta learning"
    - "learning explanatory rules from noisy data"
    - "a deep compositional framework for human like language acquisition in virtual environment"

Connected through hyperedge HE-5 (induced by source paper "compositional adam an adaptive compositional solver"):
  with members:
    - "online meta learning"
    - "a survey of optimization methods from a machine learning perspective"
    - "on the convergence of adam and beyond"
    - "derivative free order robust optimisation"
    - "toward multimodal model agnostic meta learning"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "learning to communicate implicitly by actions"
    - "accelerated method for stochastic composition optimization with nonsmooth regularization"

Connected through hyperedge HE-6 (induced by source paper "from learning to meta learning reduced training overhead and complexity for communication systems"):
  with members:
    - "deep transfer learning based downlink channel prediction for fdd massive mimo systems"
    - "meta learning with warped gradient descent"
    - "meta learning without memorization"
    - "modular meta learning with shrinkage"
    - "uncertainty in model agnostic meta learning using variational inference"
    - "on the convergence theory of gradient based model agnostic meta learning algorithms"
    - "on first order meta learning algorithms"
    - "recasting gradient based meta learning as hierarchical bayes"

Connected through hyperedge HE-7 (induced by source paper "meta learning with warped gradient descent"):
  with members:
    - "backpropamine training self modifying neural networks with differentiable neuromodulated plasticity"
    - "guided meta policy search"
    - "meta learning with differentiable convex optimization"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "meta learning representations for continual learning"
    - "on first order meta learning algorithms"
    - "understanding short horizon bias in stochastic meta optimization"
    - "recasting gradient based meta learning as hierarchical bayes"

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"
    - "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"

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)