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: "on the convergence theory of gradient based model agnostic meta learning algorithms"
  Abstract: In this paper, we study the convergence of a class of gradient-based Model-Agnostic Meta-Learning (MAML) methods and characterize their overall computational complexity as well as their best achievable level of accuracy in terms of gradient norm for nonconvex loss functions. In particular, we start with the MAML algorithm and its first order approximation (FO-MAML) and highlight the challenges that emerge in their analysis. By overcoming these challenges not only we provide the first theoretical guarantees for MAML and FO-MAML in nonconvex settings, but also we answer some of the unanswered questions for the implementation of these algorithms including how to choose their learning rate (stepsize) and the batch size for both tasks and datasets corresponding to tasks. In particular, we show that MAML can find an $\epsilon$-first-order stationary point for any positive $\epsilon$ after at most $\mathcal{O}(1/\epsilon^2)$ iterations at the expense of requiring second-order information. We also show that the FO-MAML method which ignores the second-order information required in the update of MAML cannot achieve any small desired level of accuracy, i.e, FO-MAML cannot find an $\epsilon$-first-order stationary point for any positive $\epsilon$. We further propose a new variant of the MAML algorithm called Hessian-free MAML (HF-MAML) which preserves all theoretical guarantees of MAML, without requiring access to the second-order information of loss functions.

Connected through hyperedge HE-1 (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 first order meta learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "distribution agnostic model agnostic meta learning"):
  with members:
    - "online meta learning on non convex setting"
    - "online meta learning"
    - "agnostic federated learning"
    - "adversarial attacks on graph neural networks via meta learning"
    - "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-3 (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 first order meta learning algorithms"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "bayesian model agnostic meta learning"

Connected through hyperedge HE-4 (induced by source paper "a sample complexity separation between non convex and convex meta learning"):
  with members:
    - "implicit regularization of discrete gradient dynamics in linear neural networks"
    - "decoupling adaptation from modeling with meta optimizers for meta learning"
    - "adaptive gradient based meta learning methods"
    - "learning to learn stochastic gradient descent with biased regularization"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "meta learning with implicit gradients"
    - "a mathematical theory of semantic development in deep neural networks"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-5 (induced by source paper "a collaborative learning framework via federated meta learning"):
  with members:
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "on first order meta learning algorithms"
    - "adaptive federated learning in resource constrained edge computing systems"
    - "adversarial meta learning"
    - "federated meta learning for recommendation"
    - "generalizing to unseen domains via adversarial data augmentation"
    - "an overview of multi task learning in deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "the sample complexity of meta sparse regression"):
  with members:
    - "an introductory guide to fano s inequality with applications in statistical estimation"
    - "online meta learning"
    - "meta learning a survey"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "matching networks for one shot learning"
    - "on the information theoretic limits of learning ising models"
    - "information theoretic limits of selecting binary graphical models in high dimensions"

Connected through hyperedge HE-7 (induced by source paper "decoupling adaptation from modeling with meta optimizers for meta learning"):
  with members:
    - "meta curvature"
    - "meta descent for online continual prediction"
    - "meta learning with warped gradient descent"
    - "alpha maml adaptive model agnostic meta learning"
    - "using learned optimizers to make models robust to input noise"
    - "meta learning via learned loss"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "towards understanding generalization in gradient based meta learning"

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)