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 efficient learning and inference"
  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 general feature embedding across prediction tasks. Ideally, we would like to construct feature embeddings that are tuned for the given task and even input image. 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, where the meta learner generates parameters for the feature layers in the prediction network based on a task input so that the feature embedding can be accurately adjusted for that task. We show that our TAFE-Net is highly effective in generalizing to new tasks or concepts and offers efficient prediction with low computational cost. We demonstrate the general applicability of TAFE-Net in several tasks including zero-shot/ few-shot learning and dynamic efficient prediction. Our networks exceed or match the state-of-the-art on most tasks. In particular, our approach improves the prediction accuracy of unseen attribute-object pairs by 4 to 15 points on the challenging visual attributes composition task.

Connected through hyperedge HE-1 (induced by source paper "multi site fmri analysis using privacy preserving federated learning and domain adaptation abide results"):
  with members:
    - "multi source domain adaptation for semantic segmentation"
    - "deep leakage from gradients"
    - "privacy preserving federated brain tumour segmentation"
    - "federated learning challenges methods and future directions"
    - "federated machine learning concept and applications"
    - "capacity bounded differential privacy"
    - "federated adversarial domain adaptation"
    - "private federated learning with domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "xnas neural architecture search with expert advice"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "sharpdarts faster and more accurate differentiable architecture search"
    - "introduction to online convex optimization"
    - "exploring randomly wired neural networks for image recognition"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "evaluating the search phase of neural architecture search"
    - "on the convergence of adam and beyond"
    - "cinic 10 is not imagenet or cifar 10"

Connected through hyperedge HE-3 (induced by source paper "robust extensible and fast teamed classifiers for vehicle tracking in multi camera networks"):
  with members:
    - "boxcars improving fine grained recognition of vehicles using 3 d bounding boxes in traffic surveillance"
    - "towards universal object detection by domain attention"
    - "assed a framework for identifying physical events through adaptive social sensor data filtering"
    - "vehicle re identification an efficient baseline using triplet embedding"
    - "domain adaptive attention model for unsupervised cross domain person re identification"
    - "towards real time head pose estimation exploring parameter reduced residual networks on in the wild datasets"
    - "do imagenet classifiers generalize to imagenet"
    - "bag of tricks and a strong baseline for deep person re identification"

Connected through hyperedge HE-4 (induced by source paper "ensemblenet end to end optimization of multi headed models"):
  with members:
    - "nextvlad an efficient neural network to aggregate frame level features for large scale video classification"
    - "knowledge distillation by on the fly native ensemble"
    - "deep mixture of experts via shallow embedding"
    - "large scale distributed neural network training through online distillation"
    - "slowfast networks for video recognition"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "deep mutual learning"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"

Connected through hyperedge HE-5 (induced by source paper "task driven modular networks for zero shot compositional learning"):
  with members:
    - "from classical to generalized zero shot learning a simple adaptation process"
    - "houdini lifelong learning as program synthesis"
    - "attributes as operators factorizing unseen attribute object compositions"
    - "maskconnect connectivity learning by gradient descent"
    - "the devil is in the tails fine grained classification in the wild"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "film visual reasoning with a general conditioning layer"
    - "pathnet evolution channels gradient descent in super neural networks"

Connected through hyperedge HE-6 (induced by source paper "moevc a mixture of experts voice conversion system with sparse gating mechanism for accelerating online computation"):
  with members:
    - "mosnet deep learning based objective assessment for voice conversion"
    - "deep mixture of experts via shallow embedding"
    - "voxceleb2 deep speaker recognition"
    - "data driven sparse structure selection for deep neural networks"
    - "thinet a filter level pruning method for deep neural network compression"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"
    - "pruning filters for efficient convnets"
    - "unsupervised learning of video representations using lstms"

Connected through hyperedge HE-7 (induced by source paper "learning to compose topic aware mixture of experts for zero shot video captioning"):
  with members:
    - "neural baby talk"
    - "universal neural machine translation for extremely low resource languages"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "s3d single shot multi span detector via fully 3d convolutional networks"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "decoupled novel object captioner"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-8 (induced by source paper "breaking the gridlock in mixture of experts consistent and efficient algorithms"):
  with members:
    - "deep mixture of experts via shallow embedding"
    - "learning one hidden layer neural networks with landscape design"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "attention is all you need"
    - "orthogonalized als a theoretically principled tensor decomposition algorithm for practical use"
    - "recovery guarantees for one hidden layer neural 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)