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: "online meta learning"
  Abstract: A central capability of intelligent systems is the ability to continuously build upon previous experiences to speed up and enhance learning of new tasks. Two distinct research paradigms have studied this question. Meta-learning views this problem as learning a prior over model parameters that is amenable for fast adaptation on a new task, but typically assumes the set of tasks are available together as a batch. In contrast, online (regret based) learning considers a sequential setting in which problems are revealed one after the other, but conventionally train only a single model without any task-specific adaptation. This work introduces an online meta-learning setting, which merges ideas from both the aforementioned paradigms to better capture the spirit and practice of continual lifelong learning. We propose the follow the meta leader algorithm which extends the MAML algorithm to this setting. Theoretically, this work provides an $\mathcal{O}(\log T)$ regret guarantee with only one additional higher order smoothness assumption in comparison to the standard online setting. Our experimental evaluation on three different large-scale tasks suggest that the proposed algorithm significantly outperforms alternatives based on traditional online learning approaches.

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"
    - "regularization shortcomings for continual learning"
    - "meta learning representations for continual learning"

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"
    - "meta learning with implicit gradients"
    - "on the convergence theory of gradient based model agnostic meta learning algorithms"
    - "on first order meta learning algorithms"

Connected through hyperedge HE-3 (induced by source paper "distribution agnostic model agnostic meta learning"):
  with members:
    - "online meta learning on non convex setting"
    - "agnostic federated learning"
    - "adversarial attacks on graph neural networks via meta learning"
    - "on the convergence theory of gradient based model agnostic meta learning algorithms"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "how to train your maml"
    - "robust optimization over multiple domains"

Connected through hyperedge HE-4 (induced by source paper "compositional adam an adaptive compositional solver"):
  with members:
    - "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"
    - "bayesian model agnostic meta learning"
    - "learning to communicate implicitly by actions"
    - "accelerated method for stochastic composition optimization with nonsmooth regularization"

Connected through hyperedge HE-5 (induced by source paper "incremental learning for metric based meta learners"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "incremental few shot learning for pedestrian attribute recognition"
    - "few shot learning with embedded class models and shot free meta training"
    - "meta learning with latent embedding optimization"
    - "efficient lifelong learning with a gem"
    - "incremental few shot learning with attention attractor networks"
    - "dynamic few shot visual learning without forgetting"
    - "the devil is in the tails fine grained classification in the wild"

Connected through hyperedge HE-6 (induced by source paper "provable meta learning of linear representations"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "learning to learn stochastic gradient descent with biased regularization"
    - "multi task deep neural networks for natural language understanding"
    - "cloze driven pretraining of self attention networks"
    - "provable guarantees for gradient based meta learning"
    - "characterizing and avoiding negative transfer"
    - "how to escape saddle points efficiently"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"

Connected through hyperedge HE-7 (induced by source paper "provably convergent policy gradient methods for model agnostic meta reinforcement learning"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "guided meta policy search"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "meta learning with implicit gradients"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "meta reinforcement learning of structured exploration strategies"

Connected through hyperedge HE-8 (induced by source paper "few shot learning via learning the representation provably"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "adaptive gradient based meta learning methods"
    - "learning to learn stochastic gradient descent with biased regularization"
    - "meta learning with differentiable convex optimization"
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "meta learning with differentiable closed form solvers"
    - "revisiting unreasonable effectiveness of data in deep learning era"

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)