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 learning with latent embedding optimization"
  Abstract: Gradient-based meta-learning techniques are both widely applicable and proficient at solving challenging few-shot learning and fast adaptation problems. However, they have practical difficulties when operating on high-dimensional parameter spaces in extreme low-data regimes. We show that it is possible to bypass these limitations by learning a data-dependent latent generative representation of model parameters, and performing gradient-based meta-learning in this low-dimensional latent space. The resulting approach, latent embedding optimization (LEO), decouples the gradient-based adaptation procedure from the underlying high-dimensional space of model parameters. Our evaluation shows that LEO can achieve state-of-the-art performance on the competitive miniImageNet and tieredImageNet few-shot classification tasks. Further analysis indicates LEO is able to capture uncertainty in the data, and can perform adaptation more effectively by optimizing in latent space.

Connected through hyperedge HE-1 (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 dataset a dataset of datasets for learning to learn from few examples"
    - "meta learning a survey"
    - "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-2 (induced by source paper "cross domain few shot classification via learned feature wise transformation"):
  with members:
    - "crdoco pixel level domain transfer with cross domain consistency"
    - "feature critic networks for heterogeneous domain generalization"
    - "dense classification and implanting for few shot learning"
    - "meta learning with differentiable convex optimization"
    - "progressive domain adaptation for object detection"
    - "a closer look at few shot classification"
    - "semantic image synthesis with spatially adaptive normalization"
    - "guided image to image translation with bi directional feature transformation"

Connected through hyperedge HE-3 (induced by source paper "effective diversity in population based reinforcement learning"):
  with members:
    - "novelty search for deep reinforcement learning policy network weights by action sequence edit metric distance"
    - "collaborative evolutionary reinforcement learning"
    - "provably robust blackbox optimization for reinforcement learning"
    - "behavior guided reinforcement learning"
    - "evolutionary reinforcement learning for sample efficient multiagent coordination"
    - "adapting behaviour for learning progress"
    - "attraction repulsion actor critic for continuous control reinforcement learning"
    - "a generalized framework for population based training"

Connected through hyperedge HE-4 (induced by source paper "deepemd few shot image classification with differentiable earth mover s distance and structured classifiers"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "few shot learning via saliency guided hallucination of samples"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "learning from adversarial features for few shot classification"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "meta learning with warped gradient descent"
    - "edge labeling graph neural network for few shot learning"

Connected through hyperedge HE-5 (induced by source paper "adargcn adaptive aggregation gcn for few shot learning"):
  with members:
    - "dual graph convolutional network for semantic segmentation"
    - "meta learning with differentiable convex optimization"
    - "infinite mixture prototypes for few shot learning"
    - "t gcn a temporal graph convolutional network for traffic prediction"
    - "edge labeling graph neural network for few shot learning"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "semantic graph convolutional networks for 3d human pose regression"
    - "a closer look at few shot classification"

Connected through hyperedge HE-6 (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-7 (induced by source paper "incremental object detection via meta learning"):
  with members:
    - "meta curvature"
    - "rilod near real time incremental learning for object detection at the edge"
    - "meta r cnn towards general solver for instance level few shot learning"
    - "meta learning without memorization"
    - "on first order meta learning algorithms"
    - "efficient lifelong learning with a gem"
    - "few shot object detection via feature reweighting"
    - "overcoming catastrophic forgetting with hard attention to the task"

Connected through hyperedge HE-8 (induced by source paper "tafssl task adaptive feature sub space learning for few shot classification"):
  with members:
    - "baby steps towards few shot learning with multiple semantics"
    - "multi level semantic feature augmentation for one shot learning"
    - "simpleshot revisiting nearest neighbor classification for few shot learning"
    - "meta learning with differentiable convex optimization"
    - "boosting few shot visual learning with self supervision"
    - "edge labeling graph neural network for few shot learning"
    - "finding task relevant features for few shot learning by category traversal"
    - "laso label set operations networks for multi label 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)