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: "zero shot deep domain adaptation"
  Abstract: Domain adaptation is an important tool to transfer knowledge about a task (e.g. classification) learned in a source domain to a second, or target domain. Current approaches assume that task-relevant target-domain data is available during training. We demonstrate how to perform domain adaptation when no such task-relevant target-domain data is available. To tackle this issue, we propose zero-shot deep domain adaptation (ZDDA), which uses privileged information from task-irrelevant dual-domain pairs. ZDDA learns a source-domain representation which is not only tailored for the task of interest but also close to the target-domain representation. Therefore, the source-domain task of interest solution (e.g. a classifier for classification tasks) which is jointly trained with the source-domain representation can be applicable to both the source and target representations. Using the MNIST, Fashion-MNIST, NIST, EMNIST, and SUN RGB-D datasets, we show that ZDDA can perform domain adaptation in classification tasks without access to task-relevant target-domain training data. We also extend ZDDA to perform sensor fusion in the SUN RGB-D scene classification task by simulating task-relevant target-domain representations with task-relevant source-domain data. To the best of our knowledge, ZDDA is the first domain adaptation and sensor fusion method which requires no task-relevant target-domain data. The underlying principle is not particular to computer vision data, but should be extensible to other domains.

Connected through hyperedge HE-1 (induced by source paper "sliced wasserstein discrepancy for unsupervised domain adaptation"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "variational wasserstein clustering"
    - "learning to adapt structured output space for semantic segmentation"
    - "syn2real a new benchmark forsynthetic to real visual domain adaptation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "open set domain adaptation by backpropagation"
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"
    - "generative modeling using the sliced wasserstein distance"

Connected through hyperedge HE-2 (induced by source paper "unsupervised domain adaptation of language models for reading comprehension"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "learning and evaluating general linguistic intelligence"
    - "simplified neural unsupervised domain adaptation"
    - "adagraph unifying predictive and continuous domain adaptation through graphs"
    - "multi task deep neural networks for natural language understanding"
    - "mrqa 2019 shared task evaluating generalization in reading comprehension"
    - "zero shot domain adaptation based on attribute information"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-3 (induced by source paper "adagraph unifying predictive and continuous domain adaptation through graphs"):
  with members:
    - "unsupervised domain adaptation using feature whitening and consensus loss"
    - "deep visual domain adaptation a survey"
    - "deep cocktail network multi source unsupervised domain adaptation with category shift"
    - "boosting domain adaptation by discovering latent domains"
    - "adaptive deep learning through visual domain localization"
    - "domain generalization with domain specific aggregation modules"
    - "kitting in the wild through online domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "unsupervised writer adaptation for synthetic to real handwritten word recognition"):
  with members:
    - "evaluating sequence to sequence models for handwritten text recognition"
    - "handwriting recognition in low resource scripts using adversarial learning"
    - "hwnet v2 an efficient word image representation for handwritten documents"
    - "boosting handwriting text recognition in small databases with transfer learning"
    - "adversarial discriminative domain adaptation"
    - "learning deep representations for word spotting under weak supervision"
    - "generating synthetic data for text recognition"
    - "scan attend and read end to end handwritten paragraph recognition with mdlstm attention"

Connected through hyperedge HE-5 (induced by source paper "zero shot domain adaptation based on attribute information"):
  with members:
    - "adversarial discriminative domain adaptation"
    - "visda the visual domain adaptation challenge"
    - "zero shot domain adaptation via kernel regression on the grassmannian"

Connected through hyperedge HE-6 (induced by source paper "learning local rgb to cad correspondences for object pose estimation"):
  with members:
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "bop benchmark for 6d object pose estimation"
    - "domain transfer for 3d pose estimation from color images without manual annotations"
    - "3d interpreter networks for viewer centered wireframe modeling"
    - "end to end learning of keypoint detector and descriptor for pose invariant 3d matching"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "3d pose regression using convolutional neural networks"
    - "seethrough finding chairs in heavily occluded indoor scene images"

Connected through hyperedge HE-7 (induced by source paper "matching rgb images to cad models for object pose estimation"):
  with members:
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "bop benchmark for 6d object pose estimation"
    - "domain transfer for 3d pose estimation from color images without manual annotations"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "3d pose regression using convolutional neural networks"
    - "unsupervised domain adaptation for 3d keypoint estimation via view consistency"
    - "real time seamless single shot 6d object pose prediction"
    - "self supervised intrinsic image decomposition"

Connected through hyperedge HE-8 (induced by source paper "generalizing across domains via cross gradient training"):
  with members:
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "adversarial discriminative domain adaptation"
    - "unified deep supervised domain adaptation and generalization"
    - "from a to z supervised transfer of style and content using deep neural network generators"
    - "simultaneous deep transfer across domains and tasks"
    - "deep residual learning for image recognition"
    - "domain generalization for object recognition with multi task autoencoders"
    - "intriguing properties of 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)