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: "recasting gradient based meta learning as hierarchical bayes"
  Abstract: Meta-learning allows an intelligent agent to leverage prior learning episodes as a basis for quickly improving performance on a novel task. Bayesian hierarchical modeling provides a theoretical framework for formalizing meta-learning as inference for a set of parameters that are shared across tasks. Here, we reformulate the model-agnostic meta-learning algorithm (MAML) of Finn et al. (2017) as a method for probabilistic inference in a hierarchical Bayesian model. In contrast to prior methods for meta-learning via hierarchical Bayes, MAML is naturally applicable to complex function approximators through its use of a scalable gradient descent procedure for posterior inference. Furthermore, the identification of MAML as hierarchical Bayes provides a way to understand the algorithm's operation as a meta-learning procedure, as well as an opportunity to make use of computational strategies for efficient inference. We use this opportunity to propose an improvement to the MAML algorithm that makes use of techniques from approximate inference and curvature estimation.

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"
    - "bayesian model agnostic meta learning"
    - "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 "multi step model agnostic meta learning convergence and improved algorithms"):
  with members:
    - "provably convergent policy gradient methods for model agnostic meta reinforcement learning"
    - "distribution agnostic model agnostic meta learning"
    - "es maml simple hessian free meta learning"
    - "learning to learn stochastic gradient descent with biased regularization"
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "online meta learning"
    - "meta learning with implicit gradients"
    - "on the convergence theory of gradient based model agnostic meta learning algorithms"

Connected through hyperedge HE-3 (induced by source paper "meta transfer learning for zero shot super resolution"):
  with members:
    - "blind super resolution with iterative kernel correction"
    - "singan learning a generative model from a single natural image"
    - "residual non local attention networks for image restoration"
    - "feedback network for image super resolution"
    - "on first order meta learning algorithms"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "meta learning with latent embedding optimization"
    - "image super resolution using very deep residual channel attention networks"

Connected through hyperedge HE-4 (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"
    - "learning mixtures of linear regressions with nearly optimal complexity"
    - "meta learning with latent embedding optimization"
    - "fast context adaptation via meta learning"

Connected through hyperedge HE-5 (induced by source paper "meta curvature"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "how to train your maml"
    - "autoaugment learning augmentation policies from data"
    - "low shot learning from imaginary data"
    - "promp proximal meta policy search"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-6 (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"
    - "fast context adaptation via meta learning"
    - "bayesian model agnostic meta learning"
    - "conditional neural processes"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "transductive few shot learning with meta learned confidence"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "edge labeling graph neural network for few shot learning"
    - "tapnet neural network augmented with task adaptive projection for few shot learning"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "meta learning with latent embedding optimization"
    - "fast context adaptation via meta learning"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "meta learning with differentiable closed form solvers"

Connected through hyperedge HE-8 (induced by source paper "personalized federated learning a meta learning approach"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "adaptive gradient based meta learning methods"
    - "tighter theory for local sgd on identical and heterogeneous data"
    - "one shot federated learning"
    - "improving federated learning personalization via model agnostic meta learning"
    - "on the convergence of local descent methods in federated learning"
    - "alpha maml adaptive model agnostic meta learning"
    - "fedpaq a communication efficient federated learning method with periodic averaging and quantization"

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)