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: "incremental learning through deep adaptation"
  Abstract: Given an existing trained neural network, it is often desirable to learn new capabilities without hindering performance of those already learned. Existing approaches either learn sub-optimal solutions, require joint training, or incur a substantial increment in the number of parameters for each added domain, typically as many as the original network. We propose a method called \emph{Deep Adaptation Networks} (DAN) that constrains newly learned filters to be linear combinations of existing ones. DANs precisely preserve performance on the original domain, require a fraction (typically 13\%, dependent on network architecture) of the number of parameters compared to standard fine-tuning procedures and converge in less cycles of training to a comparable or better level of performance. When coupled with standard network quantization techniques, we further reduce the parameter cost to around 3\% of the original with negligible or no loss in accuracy. The learned architecture can be controlled to switch between various learned representations, enabling a single network to solve a task from multiple different domains. We conduct extensive experiments showing the effectiveness of our method on a range of image classification tasks and explore different aspects of its behavior.

Connected through hyperedge HE-1 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "on mutual information maximization for representation learning"
    - "large scale adversarial representation learning"
    - "classification accuracy score for conditional generative models"
    - "data efficient image recognition with contrastive predictive coding"
    - "do imagenet classifiers generalize to imagenet"
    - "remixmatch semi supervised learning with distribution alignment and augmentation anchoring"
    - "meta dataset a dataset of datasets for learning to learn from few examples"

Connected through hyperedge HE-2 (induced by source paper "residual continual learning"):
  with members:
    - "knowledge transfer in deep block modular neural networks"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "memory aware synapses learning what not to forget"
    - "progressive neural networks"
    - "continual learning with deep generative replay"
    - "learning multiple visual domains with residual adapters"
    - "continual learning through synaptic intelligence"
    - "overcoming catastrophic forgetting by incremental moment matching"

Connected through hyperedge HE-3 (induced by source paper "attentive single tasking of multiple tasks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "adversarial collaboration joint unsupervised learning of depth camera motion optical flow and motion segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "taskonomy disentangling task transfer learning"
    - "unified perceptual parsing for scene understanding"
    - "efficient video object segmentation via network modulation"

Connected through hyperedge HE-4 (induced by source paper "nettailor tuning the architecture not just the weights"):
  with members:
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "darts differentiable architecture search"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "teacher student curriculum learning"

Connected through hyperedge HE-5 (induced by source paper "continual learning a comparative study on how to defy forgetting in classification tasks"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "continual learning for robotics"
    - "continual lifelong learning with neural networks a review"
    - "a comprehensive application oriented study of catastrophic forgetting in dnns"
    - "continual learning with tiny episodic memories"
    - "continual classification learning using generative models"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"

Connected through hyperedge HE-6 (induced by source paper "towards universal object detection by domain attention"):
  with members:
    - "yolov3 an incremental improvement"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "taskonomy disentangling task transfer learning"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "3d context enhanced region based convolutional neural network for end to end lesion detection"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "non local neural networks"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "efficient multi domain network learning by covariance normalization"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "boosting domain adaptation by discovering latent domains"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "efficient parametrization of multi domain deep neural networks"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "progressive neural networks"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-8 (induced by source paper "object landmark discovery through unsupervised adaptation"):
  with members:
    - "unsupervised part based disentangling of object shape and appearance"
    - "learning correspondence from the cycle consistency of time"
    - "lifting autoencoders unsupervised learning of a fully disentangled 3d morphable model using deep non rigid structure from motion"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "simple baselines for human pose estimation and tracking"
    - "unsupervised discovery of object landmarks as structural representations"
    - "ganimation anatomically aware facial animation from a single image"

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)