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: "adversarial domain adaptation for classification of prostate histopathology whole slide images"
  Abstract: Automatic and accurate Gleason grading of histopathology tissue slides is crucial for prostate cancer diagnosis, treatment, and prognosis. Usually, histopathology tissue slides from different institutions show heterogeneous appearances because of different tissue preparation and staining procedures, thus the predictable model learned from one domain may not be applicable to a new domain directly. Here we propose to adopt unsupervised domain adaptation to transfer the discriminative knowledge obtained from the source domain to the target domain without requiring labeling of images at the target domain. The adaptation is achieved through adversarial training to find an invariant feature space along with the proposed Siamese architecture on the target domain to add a regularization that is appropriate for the whole-slide images. We validate the method on two prostate cancer datasets and obtain significant classification improvement of Gleason scores as compared with the baseline models.

Connected through hyperedge HE-1 (induced by source paper "constrained domain adaptation for segmentation"):
  with members:
    - "task driven generative modeling for unsupervised domain adaptation application to x ray image segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "a novel domain adaptation framework for medical image segmentation"
    - "imposing hard constraints on deep networks promises and limitations"
    - "cycada cycle consistent adversarial domain adaptation"
    - "road reality oriented adaptation for semantic segmentation of urban scenes"
    - "adversarial discriminative domain adaptation"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"

Connected through hyperedge HE-2 (induced by source paper "an attention based multi resolution model for prostate whole slide imageclassification and localization"):
  with members:
    - "knowledge driven encode retrieve paraphrase for medical image report generation"
    - "development and validation of a deep learning algorithm for improving gleason scoring of prostate cancer"
    - "attention based deep multiple instance learning"
    - "adversarial complementary learning for weakly supervised object localization"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "coupling weak and strong supervision for classification of prostate cancer histopathology images"
    - "tell me where to look guided attention inference network"
    - "terabyte scale deep multiple instance learning for classification and localization in pathology"

Connected through hyperedge HE-3 (induced by source paper "uncertainty guided domain alignment for layer segmentation in oct images"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "unsupervised domain adaptation for automatic estimation of cardiothoracic ratio"
    - "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"
    - "joint segmentation and uncertainty visualization of retinal layers in optical coherence tomography images using bayesian deep learning"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"

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)