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: "toward multimodal model agnostic meta learning"
  Abstract: Gradient-based meta-learners such as MAML are able to learn a meta-prior from similar tasks to adapt to novel tasks from the same distribution with few gradient updates. One important limitation of such frameworks is that they seek a common initialization shared across the entire task distribution, substantially limiting the diversity of the task distributions that they are able to learn from. In this paper, we augment MAML with the capability to identify tasks sampled from a multimodal task distribution and adapt quickly through gradient updates. Specifically, we propose a multimodal MAML algorithm that is able to modulate its meta-learned prior according to the identified task, allowing faster adaptation. We evaluate the proposed model on a diverse set of problems including regression, few-shot image classification, and reinforcement learning. The results demonstrate the effectiveness of our model in modulating the meta-learned prior in response to the characteristics of tasks sampled from a multimodal distribution.

Connected through hyperedge HE-1 (induced by source paper "automated relational meta learning"):
  with members:
    - "few shot unsupervised image to image translation"
    - "learning from multiple cities a meta learning approach for spatial temporal prediction"
    - "tafe net task aware feature embeddings for low shot learning"
    - "metapred meta learning for clinical risk prediction with limited patient electronic health records"
    - "tapnet neural network augmented with task adaptive projection for few shot learning"
    - "hierarchically structured meta learning"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"

Connected through hyperedge HE-2 (induced by source paper "compositional adam an adaptive compositional solver"):
  with members:
    - "online meta learning"
    - "a survey of optimization methods from a machine learning perspective"
    - "on the convergence of adam and beyond"
    - "derivative free order robust optimisation"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "bayesian model agnostic meta learning"
    - "learning to communicate implicitly by actions"
    - "accelerated method for stochastic composition optimization with nonsmooth regularization"

Connected through hyperedge HE-3 (induced by source paper "learning adaptive classifiers synthesis for generalized few shot learning"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "meta learning with differentiable convex optimization"
    - "finding task relevant features for few shot learning by category traversal"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "a closer look at few shot classification"
    - "tapnet neural network augmented with task adaptive projection for few shot learning"
    - "meta dataset a dataset of datasets for learning to learn from few examples"

Connected through hyperedge HE-4 (induced by source paper "hierarchically structured meta learning"):
  with members:
    - "on first order meta learning algorithms"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "reptile a scalable metalearning algorithm"
    - "bayesian model agnostic meta learning"
    - "hierarchical graph representation learning with differentiable pooling"
    - "transferring knowledge across learning processes"
    - "meta learning for low resource neural machine translation"
    - "optimization landscape and expressivity of deep cnns"

Connected through hyperedge HE-5 (induced by source paper "learning to forget for meta learning"):
  with members:
    - "on first order meta learning algorithms"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "how to train your maml"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "meta learning with differentiable closed form solvers"
    - "exploring generalization in deep learning"

Connected through hyperedge HE-6 (induced by source paper "learning classifier synthesis for generalized few shot learning"):
  with members:
    - "large scale long tailed recognition in an open world"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "learning embedding adaptation for few shot learning"
    - "how to train your maml"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "low shot learning from imaginary data"
    - "prototypical networks for few shot 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)