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 adaptations for computer vision applications"
  Abstract: A basic assumption of statistical learning theory is that train and test data are drawn from the same underlying distribution. Unfortunately, this assumption doesn't hold in many applications. Instead, ample labeled data might exist in a particular `source' domain while inference is needed in another, `target' domain. Domain adaptation methods leverage labeled data from both domains to improve classification on unseen data in the target domain. In this work we survey domain transfer learning methods for various application domains with focus on recent work in Computer Vision.

Connected through hyperedge HE-1 (induced by source paper "multi purposing domain adaptation discriminators for pseudo labeling confidence"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "beyond sharing weights for deep domain adaptation"
    - "on learning invariant representation for domain adaptation"
    - "a survey of unsupervised deep domain adaptation"
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "domain confusion with self ensembling for unsupervised adaptation"
    - "autodial automatic domain alignment layers"

Connected through hyperedge HE-2 (induced by source paper "demystifying neural style transfer"):
  with members:
    - "perceptual losses for real time style transfer and super resolution"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "semantic style transfer and turning two bit doodles into fine artworks"
    - "artistic style transfer for videos"
    - "unsupervised domain adaptation with residual transfer networks"
    - "learning transferable features with deep adaptation networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "revisiting batch normalization for practical domain adaptation"):
  with members:
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deep coral correlation alignment for deep domain adaptation"
    - "domain separation networks"
    - "unsupervised domain adaptation with residual transfer networks"
    - "return of frustratingly easy domain adaptation"
    - "simultaneous deep transfer across domains and tasks"
    - "learning transferable features with deep adaptation networks"
    - "deep residual learning for image recognition"

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)