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: "learning more universal representations for transfer learning"
  Abstract: Transfer learning is commonly used to address the problem of the prohibitive need in annotated data when one want to classify visual content with a Convolutional Neural Network (CNN). We address the problem of the universality of the CNN-based representation of images in such a context. The state-of-the-art consists in diversifying the source problem on which the CNN is learned. It reduces the cost for the target problem but still requires a large amount of efforts to satisfy the source problem needs in annotated data. We propose an unified framework of the methods that improve the universality by diversifying the source problem. We also propose two methods that improve the universality but pay special attention to limit the need of annotated data. Finally, we propose a new evaluation protocol to compare the ability of CNN-based representation to tackle the problem of universality. It demonstrates the interest of our work on 10 publicly available benchmarks, relating to a variety of visual classification problems.

Connected through hyperedge HE-1 (induced by source paper "crl class representative learning for image classification"):
  with members:
    - "unifying unsupervised domain adaptation and zero shot visual recognition"
    - "meta transfer networks for zero shot learning"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "rethinking imagenet pre training"
    - "do better imagenet models transfer better"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "towards universal representation for unseen action recognition"
    - "complementary attributes a new clue to zero shot learning"

Connected through hyperedge HE-2 (induced by source paper "scail classifier weights scaling for class incremental learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "incremental learning with maximum entropy regularization rethinking forgetting and intransigence"
    - "growing a brain fine tuning by increasing model capacity"
    - "large scale incremental learning"
    - "m2kd multi model and multi level knowledge distillation for incremental learning"
    - "do better imagenet models transfer better"
    - "revisiting distillation and incremental classifier learning"
    - "deesil deep shallow incremental learning"

Connected through hyperedge HE-3 (induced by source paper "towards universal representation learning for deep face recognition"):
  with members:
    - "towards universal object detection by domain attention"
    - "probabilistic face embeddings"
    - "arcface additive angular margin loss for deep face recognition"
    - "multicolumn networks for face recognition"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "additive margin softmax for face verification"
    - "on calibration of modern neural networks"
    - "universal representations the missing link between faces text planktons and cat breeds"

Connected through hyperedge HE-4 (induced by source paper "generative collaborative networks for single image super resolution"):
  with members:
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "perceptual losses for real time style transfer and super resolution"
    - "accelerating the super resolution convolutional neural network"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "amortised map inference for image super resolution"
    - "generating images with perceptual similarity metrics based on deep networks"

Connected through hyperedge HE-5 (induced by source paper "enhanced transfer learning with imagenet trained classification layer"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "a growing long term episodic semantic memory"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning visual features from large weakly supervised data"
    - "multi scale recognition with dag cnns"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "semantic and visual similarities for efficient knowledge transfer in cnn training"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "taskonomy disentangling task transfer learning"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "what makes imagenet good for transfer learning"
    - "cnn rnn a unified framework for multi label image classification"
    - "deep residual learning for image recognition"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "depth augmented networks with optimal fine tuning"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "a growing long term episodic semantic memory"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning visual features from large weakly supervised data"
    - "multi scale recognition with dag cnns"
    - "going deeper with convolutions"

Connected through hyperedge HE-8 (induced by source paper "where is the fake patch wise supervised gans for texture inpainting"):
  with members:
    - "adversarial inpainting of medical image modalities"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "which training methods for gans do actually converge"
    - "image to image translation with conditional adversarial networks"
    - "context encoders feature learning by inpainting"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"

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)