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: "domain symmetric networks for adversarial domain adaptation"
  Abstract: Unsupervised domain adaptation aims to learn a model of classifier for unlabeled samples on the target domain, given training data of labeled samples on the source domain. Impressive progress is made recently by learning invariant features via domain-adversarial training of deep networks. In spite of the recent progress, domain adaptation is still limited in achieving the invariance of feature distributions at a finer category level. To this end, we propose in this paper a new domain adaptation method called Domain-Symmetric Networks (SymNets). The proposed SymNet is based on a symmetric design of source and target task classifiers, based on which we also construct an additional classifier that shares with them its layer neurons. To train the SymNet, we propose a novel adversarial learning objective whose key design is based on a two-level domain confusion scheme, where the category-level confusion loss improves over the domain-level one by driving the learning of intermediate network features to be invariant at the corresponding categories of the two domains. Both domain discrimination and domain confusion are implemented based on the constructed additional classifier. Since target samples are unlabeled, we also propose a scheme of cross-domain training to help learn the target classifier. Careful ablation studies show the efficacy of our proposed method. In particular, based on commonly used base networks, our SymNets achieve the new state of the art on three benchmark domain adaptation datasets.

Connected through hyperedge HE-1 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice"):
  with members:
    - "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"
    - "beyond sharing weights for deep domain adaptation"
    - "bridging theory and algorithm for domain adaptation"
    - "learning to transfer examples for partial domain adaptation"
    - "open set domain adaptation for image and action recognition"

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"
    - "bridging theory and algorithm for domain adaptation"

Connected through hyperedge HE-3 (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"
    - "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"
    - "learning to adapt structured output space for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "discriminative adversarial domain adaptation"):
  with members:
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "open set domain adaptation for image and action recognition"
    - "transferrable prototypical networks for unsupervised domain adaptation"
    - "unsupervised visual domain adaptation a deep max margin gaussian process approach"
    - "looking back at labels a class based domain adaptation technique"
    - "fine grained visual categorization using meta learning optimization with sample selection of auxiliary data"
    - "gotta adapt em all joint pixel and feature level domain adaptation for recognition in the wild"
    - "syn2real a new benchmark forsynthetic to real visual domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "a comprehensive survey on transfer learning"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "transfer learning toolkit primers and benchmarks"
    - "domain agnostic learning with disentangled representations"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "transfer learning with dynamic adversarial adaptation network"
    - "transferrable prototypical networks for unsupervised domain adaptation"
    - "deep visual domain adaptation a survey"
    - "a survey on deep transfer learning"

Connected through hyperedge HE-6 (induced by source paper "collaborative training of balanced random forests for open set domain adaptation"):
  with members:
    - "weakly supervised open set domain adaptation by dual domain collaboration"
    - "algorithms and theory for multiple source adaptation"
    - "open set domain adaptation by backpropagation"
    - "boosting domain adaptation by discovering latent domains"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "deep hashing network for unsupervised domain adaptation"
    - "partial transfer learning with selective adversarial networks"
    - "incremental boosting convolutional neural network for facial action unit recognition"

Connected through hyperedge HE-7 (induced by source paper "unsupervised domain adaptation via structured prediction based selective pseudo labeling"):
  with members:
    - "unifying unsupervised domain adaptation and zero shot visual recognition"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "multi adversarial domain adaptation"
    - "joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation"
    - "progressive feature alignment for unsupervised domain adaptation"
    - "deep hashing network for unsupervised domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "generate to adapt aligning domains using generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "domain generalization using a mixture of multiple latent domains"):
  with members:
    - "crdoco pixel level domain transfer with cross domain consistency"
    - "domain generalization by solving jigsaw puzzles"
    - "deep cocktail network multi source unsupervised domain adaptation with category shift"
    - "boosting domain adaptation by discovering latent domains"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "demystifying neural style transfer"
    - "learning to generalize meta learning for domain generalization"
    - "arbitrary style transfer in real time with adaptive instance normalization"

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)