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 "pnp adanet plug and play adversarial domain adaptation network with a benchmark at cross modality cardiac segmentation"):
  Abstract: Deep convolutional networks have demonstrated the state-of-the-art performance on various medical image computing tasks. Leveraging images from different modalities for the same analysis task holds clinical benefits. However, the generalization capability of deep models on test data with different distributions remain as a major challenge. In this paper, we propose the PnPAdaNet (plug-and-play adversarial domain adaptation network) for adapting segmentation networks between different modalities of medical images, e.g., MRI and CT. We propose to tackle the significant domain shift by aligning the feature spaces of source and target domains in an unsupervised manner. Specifically, a domain adaptation module flexibly replaces the early encoder layers of the source network, and the higher layers are shared between domains. With adversarial learning, we build two discriminators whose inputs are respectively multi-level features and predicted segmentation masks. We have validated our domain adaptation method on cardiac structure segmentation in unpaired MRI and CT. The experimental results with comprehensive ablation studies demonstrate the excellent efficacy of our proposed PnP-AdaNet. Moreover, we introduce a novel benchmark on the cardiac dataset for the task of unsupervised cross-modality domain adaptation. We will make our code and database publicly available, aiming to promote future studies on this challenging yet important research topic in medical imaging.

Includes members:
  Member "task driven generative modeling for unsupervised domain adaptation application to x ray image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "one to one mapping for unpaired image to image translation")
  Member "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "one to one mapping for unpaired image to image translation")
  Member "semantic aware generative adversarial nets for unsupervised domain adaptation in chest x ray segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on direct distribution matching for adapting segmentation networks")
  Member "distribution matching losses can hallucinate features in medical image translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "visualisation of medical image fusion and translation for accurate diagnosis of high grade gliomas")
  Member "generalization bounds for unsupervised cross domain mapping with wgans":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "alignflow cycle consistent learning from multiple domains via normalizing flows")
  Member "domain and geometry agnostic cnns for left atrium segmentation in 3d ultrasound":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "when unseen domain generalization is unnecessary rethinking data augmentation")
  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 "adversarial discriminative domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")

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)