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: "task2vec task embedding for meta learning"
  Abstract: We introduce a method to provide vectorial representations of visual classification tasks which can be used to reason about the nature of those tasks and their relations. Given a dataset with ground-truth labels and a loss function defined over those labels, we process images through a "probe network" and compute an embedding based on estimates of the Fisher information matrix associated with the probe network parameters. This provides a fixed-dimensional embedding of the task that is independent of details such as the number of classes and does not require any understanding of the class label semantics. We demonstrate that this embedding is capable of predicting task similarities that match our intuition about semantic and taxonomic relations between different visual tasks (e.g., tasks based on classifying different types of plants are similar) We also demonstrate the practical value of this framework for the meta-task of selecting a pre-trained feature extractor for a new task. We present a simple meta-learning framework for learning a metric on embeddings that is capable of predicting which feature extractors will perform well. Selecting a feature extractor with task embedding obtains a performance close to the best available feature extractor, while costing substantially less than exhaustively training and evaluating on all available feature extractors.

Connected through hyperedge HE-1 (induced by source paper "revisiting meta learning as supervised learning"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "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 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-2 (induced by source paper "rethinking the hyperparameters for fine tuning"):
  with members:
    - "rethinking imagenet pre training"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "do better imagenet models transfer better"
    - "bag of tricks for image classification with convolutional neural networks"
    - "toward deeper understanding of nonconvex stochastic optimization with momentum using diffusion approximations"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "domain adaptive transfer learning with specialist models"
    - "autoaugment learning augmentation policies from data"

Connected through hyperedge HE-3 (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"
    - "fixynn efficient hardware for mobile computer vision via transfer learning"
    - "meta dataset a dataset of datasets for learning to learn from few examples"

Connected through hyperedge HE-4 (induced by source paper "geometric dataset distances via optimal transport"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "dynamics and reachability of learning tasks"
    - "hierarchical optimal transport for document representation"
    - "transferability and hardness of supervised classification tasks"
    - "learning embeddings into entropic wasserstein spaces"
    - "gromov wasserstein alignment of word embedding spaces"
    - "deep learning for classical japanese literature"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (induced by source paper "transferability and hardness of supervised classification tasks"):
  with members:
    - "registration free face ssd single shot analysis of smiles facial attributes and affect in the wild"
    - "beyond sharing weights for deep domain adaptation"
    - "on measuring the iconicity of a face"
    - "the information complexity of learning tasks their structure and their distance"
    - "toward understanding catastrophic forgetting in continual learning"
    - "regularized learning for domain adaptation under label shifts"
    - "meta learning with latent embedding optimization"
    - "a modulation module for multi task learning with applications in image retrieval"

Connected through hyperedge HE-6 (induced by source paper "boosting supervision with self supervision for few shot learning"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "a closer look at few shot classification"
    - "revisiting self supervised visual representation learning"
    - "attentive single tasking of multiple tasks"
    - "deep clustering for unsupervised learning of visual features"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-7 (induced by source paper "towards a general model of knowledge for facial analysis by multi source transfer learning"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "delta deep learning transfer using feature map with attention for convolutional networks"
    - "mfas multimodal fusion architecture search"
    - "best sources forward domain generalization through source specific nets"
    - "measure manifold learning and optimization a theory of neural networks"
    - "deep clustering for unsupervised learning of visual features"
    - "taskonomy disentangling task transfer learning"
    - "an occam s razor view on learning audiovisual emotion recognition with small training sets"

Connected through hyperedge HE-8 (induced by source paper "zero shot task adaptation by homoiconic meta mapping"):
  with members:
    - "lgm net learning to generate matching networks for few shot learning"
    - "online meta learning"
    - "learnable embedding space for efficient neural architecture compression"
    - "zero shot task transfer"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "meta learning with latent embedding optimization"
    - "universal successor features approximators"
    - "graph hypernetworks for neural architecture search"

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)