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: "tafe net task aware feature embeddings for low shot learning"
  Abstract: Learning good feature embeddings for images often requires substantial training data. As a consequence, in settings where training data is limited (e.g., few-shot and zero-shot learning), we are typically forced to use a generic feature embedding across various tasks. Ideally, we want to construct feature embeddings that are tuned for the given task. In this work, we propose Task-Aware Feature Embedding Networks (TAFE-Nets) to learn how to adapt the image representation to a new task in a meta learning fashion. Our network is composed of a meta learner and a prediction network. Based on a task input, the meta learner generates parameters for the feature layers in the prediction network so that the feature embedding can be accurately adjusted for that task. We show that TAFE-Net is highly effective in generalizing to new tasks or concepts and evaluate the TAFE-Net on a range of benchmarks in zero-shot and few-shot learning. Our model matches or exceeds the state-of-the-art on all tasks. In particular, our approach improves the prediction accuracy of unseen attribute-object pairs by 4 to 15 points on the challenging visual attribute-object composition task.

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"
    - "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"
    - "toward multimodal model agnostic meta learning"
    - "reptile a scalable metalearning algorithm"

Connected through hyperedge HE-2 (induced by source paper "a new meta baseline for few shot learning"):
  with members:
    - "a closer look at few shot classification"
    - "meta learning for semi supervised few shot classification"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"
    - "dropblock a regularization method for convolutional networks"
    - "meta transfer learning for few shot learning"
    - "dynamic few shot visual learning without forgetting"
    - "low shot learning with imprinted weights"

Connected through hyperedge HE-3 (induced by source paper "frustratingly simple few shot object detection"):
  with members:
    - "a baseline for few shot image classification"
    - "lvis a dataset for large vocabulary instance segmentation"
    - "a closer look at few shot classification"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "few shot object detection via feature reweighting"
    - "dynamic few shot visual learning without forgetting"
    - "low shot learning with imprinted weights"

Connected through hyperedge HE-4 (induced by source paper "domain aware dynamic networks"):
  with members:
    - "test time training for out of distribution generalization"
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "domain randomization and pyramid consistency simulation to real generalization without accessing target domain data"
    - "multi source domain adaptation for semantic segmentation"
    - "condconv conditionally parameterized convolutions for efficient inference"
    - "metapruning meta learning for automatic neural network channel pruning"
    - "searching for mobilenetv3"
    - "single path one shot neural architecture search with uniform sampling"

Connected through hyperedge HE-5 (induced by source paper "deep learning on small datasets without pre training using cosine loss"):
  with members:
    - "hierarchy based image embeddings for semantic image retrieval"
    - "tencent ml images a large scale multi label image database for visual representation learning"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "bag of tricks for image classification with convolutional neural networks"
    - "cosface large margin cosine loss for deep face recognition"
    - "dada deep adversarial data augmentation for extremely low data regime classification"
    - "directional statistics based deep metric learning for image classification and retrieval"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "task aware deep sampling for feature generation"):
  with members:
    - "low shot learning via covariance preserving adversarial augmentation networks"
    - "attributes as operators factorizing unseen attribute object compositions"
    - "multi modal cycle consistent generalized zero shot learning"
    - "a style based generator architecture for generative adversarial networks"
    - "low shot learning from imaginary data"
    - "dynamic few shot visual learning without forgetting"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "deep layer aggregation"

Connected through hyperedge HE-7 (induced by source paper "few shot learning with contextual cueing for object recognition in complex scenes"):
  with members:
    - "baby steps towards few shot learning with multiple semantics"
    - "dense classification and implanting for few shot learning"
    - "context aware zero shot learning for object recognition"
    - "few shot learning via saliency guided hallucination of samples"
    - "few shot learning with localization in realistic settings"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "image deformation meta networks for one shot learning"
    - "adaptive cross modal few shot learning"

Connected through hyperedge HE-8 (induced by source paper "stochastic conditional generative networks with basis decomposition"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "diversity sensitive conditional generative adversarial networks"
    - "multi agent diverse generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "texturegan controlling deep image synthesis with texture patches"
    - "mathematics of deep learning"
    - "few shot image recognition by predicting parameters from activations"
    - "unpaired image to image translation using cycle consistent adversarial networks"

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.CV(Computer Vision and Pattern Recognition)