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: "meta dataset a dataset of datasets for learning to learn from few examples"
  Abstract: Few-shot classification refers to learning a classifier for new classes given only a few examples. While a plethora of models have emerged to tackle it, we find the procedure and datasets that are used to assess their progress lacking. To address this limitation, we propose Meta-Dataset: a new benchmark for training and evaluating models that is large-scale, consists of diverse datasets, and presents more realistic tasks. We experiment with popular baselines and meta-learners on Meta-Dataset, along with a competitive method that we propose. We analyze performance as a function of various characteristics of test tasks and examine the models' ability to leverage diverse training sources for improving their generalization. We also propose a new set of baselines for quantifying the benefit of meta-learning in Meta-Dataset. Our extensive experimentation has uncovered important research challenges and we hope to inspire work in these directions.

Connected through hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies"):
  with members:
    - "on empirical comparisons of optimizers for deep learning"
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "on the variance of the adaptive learning rate and beyond"
    - "the visual task adaptation benchmark"
    - "using learned optimizers to make models robust to input noise"
    - "deepobs a deep learning optimizer benchmark suite"
    - "nas bench 101 towards reproducible neural architecture search"

Connected through hyperedge HE-2 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "on mutual information maximization for representation learning"
    - "large scale adversarial representation learning"
    - "classification accuracy score for conditional generative models"
    - "data efficient image recognition with contrastive predictive coding"
    - "do imagenet classifiers generalize to imagenet"
    - "remixmatch semi supervised learning with distribution alignment and augmentation anchoring"
    - "revisiting self supervised visual representation learning"

Connected through hyperedge HE-3 (induced by source paper "revisiting meta learning as supervised learning"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "task2vec task embedding for meta learning"
    - "a closer look at few shot classification"
    - "the information complexity of learning tasks their structure and their distance"
    - "meta learning a survey"
    - "meta learning with latent embedding optimization"
    - "meta learning transferable active learning policies by deep reinforcement learning"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "optimized generic feature learning for few shot classification across domains"):
  with members:
    - "a baseline for few shot image classification"
    - "meta learning with differentiable convex optimization"
    - "large scale adversarial representation learning"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "fast autoaugment"
    - "autodispnet improving disparity estimation with automl"
    - "a closer look at few shot classification"

Connected through hyperedge HE-5 (induced by source paper "leep a new measure to evaluate transferability of learned representations"):
  with members:
    - "dataset2vec learning dataset meta features"
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "a baseline for few shot image classification"
    - "which tasks should be learned together in multi task learning"
    - "p2l predicting transfer learning for images and semantic relations"
    - "transferability and hardness of supervised classification tasks"
    - "task2vec task embedding for meta learning"
    - "fixynn efficient hardware for mobile computer vision via transfer learning"

Connected through hyperedge HE-6 (induced by source paper "meta learning for mixed linear regression"):
  with members:
    - "learning mixtures of linear regressions in subexponential time via fourier moments"
    - "sublinear optimal policy value estimation in contextual bandits"
    - "meta learning with implicit gradients"
    - "estimating learnability in the sublinear data regime"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "learning mixtures of linear regressions with nearly optimal complexity"
    - "meta learning with latent embedding optimization"
    - "fast context adaptation via meta learning"

Connected through hyperedge HE-7 (induced by source paper "transductive few shot learning with meta learned confidence"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "edge labeling graph neural network for few shot learning"
    - "tapnet neural network augmented with task adaptive projection for few shot learning"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"
    - "fast context adaptation via meta learning"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "meta learning with differentiable closed form solvers"

Connected through hyperedge HE-8 (induced by source paper "meta learning initializations for low resource drug discovery"):
  with members:
    - "infograph unsupervised and semi supervised graph level representation learning via mutual information maximization"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "pre training graph neural networks"
    - "meta learning a survey"
    - "potentialnet for molecular property prediction"
    - "how powerful are graph neural networks"
    - "neural message passing for quantum chemistry"
    - "moleculenet a benchmark for molecular machine 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)