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: "distribution agnostic model agnostic meta learning"
  Abstract: The Model-Agnostic Meta-Learning (MAML) algorithm \citep{finn2017model} has been celebrated for its efficiency and generality, as it has demonstrated success in quickly learning the parameters of an arbitrary learning model. However, MAML implicitly assumes that the tasks come from a particular distribution, and optimizes the expected (or sample average) loss over tasks drawn from this distribution. Here, we amend this limitation of MAML by reformulating the objective function as a min-max problem, where the maximization is over the set of possible distributions over tasks. Our proposed algorithm is the first distribution-agnostic and model-agnostic meta-learning method, and we show that it converges to an $\epsilon$-accurate point at the rate of $\mathcal{O}(1/\epsilon^2)$ in the convex setting and to an $(\epsilon, \delta)$-stationary point at the rate of $\mathcal{O}(\max\{1/\epsilon^5, 1/\delta^5\})$ in nonconvex settings. We also provide numerical experiments that demonstrate the worst-case superiority of our algorithm in comparison to MAML.

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"
    - "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"
    - "on first order meta learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "no regret non convex online meta learning"):
  with members:
    - "online meta learning"
    - "on first order meta learning algorithms"
    - "how to train your maml"
    - "efficient regret minimization in non convex games"
    - "matching networks for one shot learning"
    - "you only look once unified real time object detection"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

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)