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: "auto meta automated gradient based meta learner search"
  Abstract: Fully automating machine learning pipelines is one of the key challenges of current artificial intelligence research, since practical machine learning often requires costly and time-consuming human-powered processes such as model design, algorithm development, and hyperparameter tuning. In this paper, we verify that automated architecture search synergizes with the effect of gradient-based meta learning. We adopt the progressive neural architecture search \cite{liu:pnas_google:DBLP:journals/corr/abs-1712-00559} to find optimal architectures for meta-learners. The gradient based meta-learner whose architecture was automatically found achieved state-of-the-art results on the 5-shot 5-way Mini-ImageNet classification problem with $74.65\%$ accuracy, which is $11.54\%$ improvement over the result obtained by the first gradient-based meta-learner called MAML \cite{finn:maml:DBLP:conf/icml/FinnAL17}. To our best knowledge, this work is the first successful neural architecture search implementation in the context of meta learning.

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"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "bayesian model agnostic meta learning"
    - "how to train your maml"
    - "understanding training and generalization in deep learning by fourier analysis"
    - "online learning rate adaptation with hypergradient descent"

Connected through hyperedge HE-2 (induced by source paper "metadapt meta learned task adaptive architecture for few shot classification"):
  with members:
    - "baby steps towards few shot learning with multiple semantics"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "boosting few shot visual learning with self supervision"
    - "few example object detection with model communication"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "laso label set operations networks for multi label few shot learning"

Connected through hyperedge HE-3 (induced by source paper "meta learning with implicit gradients"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "a differentiable augmented lagrangian method for bilevel nonlinear optimization"
    - "infinite mixture prototypes for few shot learning"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "on first order meta learning algorithms"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"

Connected through hyperedge HE-4 (induced by source paper "meta learning of neural architectures for few shot learning"):
  with members:
    - "meta learning with warped gradient descent"
    - "searching for a robust neural architecture in four gpu hours"
    - "understanding and robustifying differentiable architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "autodispnet improving disparity estimation with automl"
    - "automated design of error resilient and hardware efficient deep neural networks"
    - "fast context adaptation via meta learning"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"

Connected through hyperedge HE-5 (induced by source paper "provable guarantees for gradient based meta learning"):
  with members:
    - "learning to learn stochastic gradient descent with biased regularization"
    - "introduction to online convex optimization"
    - "online gradient based mixtures for transfer modulation in meta learning"
    - "fast rates for online gradient descent without strong convexity via hoffman s bound"
    - "prototypical networks for few shot learning"
    - "meta learning and universality deep representations and gradient descent can approximate any learning algorithm"
    - "continuous adaptation via meta learning in nonstationary and competitive environments"
    - "meta sgd learning to learn quickly for few shot learning"

Connected through hyperedge HE-6 (induced by source paper "unsupervised learning via meta learning"):
  with members:
    - "understanding and improving interpolation in autoencoders via an adversarial regularizer"
    - "unsupervised meta learning for reinforcement learning"
    - "deep clustering for unsupervised learning of visual features"
    - "probabilistic model agnostic meta learning"
    - "universal language model fine tuning for text classification"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "representation learning with contrastive predictive coding"
    - "supervising unsupervised 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)