human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "multi step model agnostic meta learning convergence and improved algorithms"):
  Abstract: As a popular meta-learning approach, the model-agnostic meta-learning (MAML) algorithm has been widely used due to its simplicity and effectiveness. However, the convergence of the general multi-step MAML still remains unexplored. In this paper, we develop a new theoretical framework, under which we characterize the convergence rate and the computational complexity of multi-step MAML. Our results indicate that $N$-step MAML attains the convergence with linearly increasing complexity with $N$ under a properly chosen inner stepsize. We then take a further step to develop a more efficient Hessian-free MAML. We first show that the existing zeroth-order Hessian estimator contains a constant-level estimation error so that the MAML algorithm can perform unstably. To address this issue, we propose a novel Hessian estimator via a gradient-based Gaussian smoothing method, and show that it achieves a much smaller estimation bias and variance, and the resulting algorithm achieves the same performance guarantee as the original MAML under mild conditions. Our experiments validate our theory and demonstrate the effectiveness of the proposed Hessian estimator.

Includes members:
  Member "provably convergent policy gradient methods for model agnostic meta reinforcement learning":
    also connected through other hyperedges:
      (none)
  Member "distribution agnostic model agnostic meta learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "no regret non convex online meta learning")
  Member "es maml simple hessian free meta learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rapidly adaptable legged robots via evolutionary meta learning")
  Member "learning to learn stochastic gradient descent with biased regularization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "provable meta learning of linear representations")
  Member "meta learning for low resource natural language generation in task oriented dialogue systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "online meta learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "meta learning with implicit gradients":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepemd few shot image classification with differentiable earth mover s distance and structured classifiers")
  Member "on the convergence theory of gradient based model agnostic meta learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distribution agnostic model agnostic meta learning")

Question: Please tell me which class the source paper 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)