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: "larger norm more transferable an adaptive feature norm approach for unsupervised domain adaptation"
  Abstract: Domain adaptation enables the learner to safely generalize into novel environments by mitigating domain shifts across distributions. Previous works may not effectively uncover the underlying reasons that would lead to the drastic model degradation on the target task. In this paper, we empirically reveal that the erratic discrimination of the target domain mainly stems from its much smaller feature norms with respect to that of the source domain. To this end, we propose a novel parameter-free Adaptive Feature Norm approach. We demonstrate that progressively adapting the feature norms of the two domains to a large range of values can result in significant transfer gains, implying that those task-specific features with larger norms are more transferable. Our method successfully unifies the computation of both standard and partial domain adaptation with more robustness against the negative transfer issue. Without bells and whistles but a few lines of code, our method substantially lifts the performance on the target task and exceeds state-of-the-arts by a large margin (11.5% on Office-Home and 17.1% on VisDA2017). We hope our simple yet effective approach will shed some light on the future research of transfer learning. Code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation"):
  with members:
    - "drop to adapt learning discriminative features for unsupervised domain adaptation"
    - "domain agnostic learning with disentangled representations"
    - "towards federated learning at scale system design"
    - "when does label smoothing help"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "pseudo labeling curriculum for unsupervised domain adaptation"
    - "learning to transfer examples for partial domain adaptation"
    - "cluster alignment with a teacher for unsupervised domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "unsupervised domain adaptation via structurally regularized deep clustering"):
  with members:
    - "unsupervised multi class domain adaptation theory algorithms and practice"
    - "discriminative adversarial domain adaptation"
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "orthogonal deep neural networks"
    - "unsupervised domain adaptation via regularized conditional alignment"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "domain specific batch normalization for unsupervised domain adaptation"
    - "domain symmetric networks for adversarial domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "domain adaptive ensemble learning"):
  with members:
    - "fixmatch simplifying semi supervised learning with consistency and confidence"
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "episodic training for domain generalization"
    - "consistency regularization for generative adversarial networks"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "randaugment practical data augmentation with no separate search"
    - "domain generalization by solving jigsaw puzzles"
    - "semi supervised domain adaptation via minimax entropy"

Connected through hyperedge HE-4 (induced by source paper "a balanced and uncertainty aware approach for partial domain adaptation"):
  with members:
    - "complement objective training"
    - "tackling partial domain adaptation with self supervision"
    - "learning to transfer examples for partial domain adaptation"
    - "virtual mixup training for unsupervised domain adaptation"
    - "transfer adaptation learning a decade survey"
    - "semi supervised learning by augmented distribution alignment"
    - "characterizing and avoiding negative transfer"
    - "importance weighted adversarial nets for partial domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "meta learning across meta tasks for few shot learning"):
  with members:
    - "cross domain few shot classification via learned feature wise transformation"
    - "learning what and where to transfer"
    - "charting the right manifold manifold mixup for few shot learning"
    - "infinite mixture prototypes for few shot learning"
    - "on minimum discrepancy estimation for deep domain adaptation"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "bridging theory and algorithm for domain adaptation"
    - "transductive episodic wise adaptive metric for few shot learning"

Connected through hyperedge HE-6 (induced by source paper "universal domain adaptation through self supervision"):
  with members:
    - "unsupervised deep learning by neighbourhood discovery"
    - "momentum contrast for unsupervised visual representation learning"
    - "domain specific batch normalization for unsupervised domain adaptation"
    - "domain generalization by solving jigsaw puzzles"
    - "bridging theory and algorithm for domain adaptation"
    - "learning to transfer examples for partial domain adaptation"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "semi supervised domain adaptation via minimax entropy"

Connected through hyperedge HE-7 (induced by source paper "bi directional generation for unsupervised domain adaptation"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "adagraph unifying predictive and continuous domain adaptation through graphs"
    - "domain symmetric networks for adversarial domain adaptation"
    - "attending to discriminative certainty for domain adaptation"
    - "transferrable prototypical networks for unsupervised domain adaptation"
    - "unsupervised domain adaptation using feature whitening and consensus loss"
    - "fully convolutional adaptation networks for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "an adversarial perturbation oriented domain adaptation approach for semantic segmentation"):
  with members:
    - "domain adaptation with asymmetrically relaxed distribution alignment"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"
    - "advent adversarial entropy minimization for domain adaptation in semantic segmentation"
    - "generalizing to unseen domains via adversarial data augmentation"
    - "no more discrimination cross city adaptation of road scene segmenters"
    - "cycada cycle consistent adversarial domain adaptation"

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)