human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "e uda efficient unsupervised domain adaptation for cross site medical image segmentation"):
  Abstract: Domain adaptation in healthcare data is a potentially critical component in making computer-aided diagnostic systems applicable cross multiple sites and imaging scanners. In this paper, we propose an efficient unsupervised domain adaptation framework for robust image segmentation cross multiple similar domains. We enforce our algorithm to not only adapt to the new domains via an adversarial optimization, rejecting unlikely segmentation patterns, but also to maintain its performance on the source training data, by incorporating both semantic and boundary information into the data distributions. Further, as we do not have labels for the transfer domain, we propose a new quality score for the adaptation process, and strategies to retrain the diagnostic algorithm in a stable fashion. Using multi-centric data from a public benchmark for brain lesion segmentation, we demonstrate that recalibrating on just few unlabeled image sets from the target domain improves segmentation accuracies drastically, with performances almost similar to those from algorithms trained on fresh and fully annotated data from the test domain.

Includes members:
  Member "evaluation of algorithms for multi modality whole heart segmentation an open access grand challenge":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unpaired multi modal segmentation via knowledge distillation")
  Member "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning cross domain generalizable features by representation disentanglement")
  Member "standardized assessment of automatic segmentation of white matter hyperintensities and results of the wmh segmentation challenge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "diamondgan unified multi modal generative adversarial networks for mri sequences synthesis")
  Member "boundary aware feature propagation for scene segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cars can t fly up in the sky improving urban scene segmentation via height driven attention networks")
  Member "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "brain metastasis segmentation network trained with robustness to annotations with multiple false negatives")
  Member "fully convolutional network ensembles for white matter hyperintensities segmentation in mr images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bridging the gap between ai and healthcare sides towards developing clinically relevant ai powered diagnosis systems")
  Member "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "boundary weighted domain adaptive neural network for prostate mr image segmentation")
  Member "cycada cycle consistent adversarial domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a novel framework for selection of gans for an application")

Question: Please tell me which class the source paper 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)