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: "detach and adapt learning cross domain disentangled deep representation"
  Abstract: While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular image attributes can be identified and manipulated. However, one cannot easily address this task without observing ground truth annotation for the training data. To address this problem, we propose a novel deep learning model of Cross-Domain Representation Disentangler (CDRD). By observing fully annotated source-domain data and unlabeled target-domain data of interest, our model bridges the information across data domains and transfers the attribute information accordingly. Thus, cross-domain joint feature disentanglement and adaptation can be jointly performed. In the experiments, we provide qualitative results to verify our disentanglement capability. Moreover, we further confirm that our model can be applied for solving classification tasks of unsupervised domain adaptation, and performs favorably against state-of-the-art image disentanglement and translation methods.

Connected through hyperedge HE-1 (induced by source paper "learning cross domain generalizable features by representation disentanglement"):
  with members:
    - "a two step disentanglement method"
    - "domain agnostic learning with disentangled representations"
    - "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "semi supervised domain adaptation via minimax entropy"
    - "domain generalization via model agnostic learning of semantic features"
    - "mixmatch a holistic approach to semi supervised learning"
    - "representation disentanglement for multi task learning with application to fetal ultrasound"

Connected through hyperedge HE-2 (induced by source paper "mind the gap enlarging the domain gap in open set domain adaptation"):
  with members:
    - "attract or distract exploit the margin of open set"
    - "weakly supervised open set domain adaptation by dual domain collaboration"
    - "open set domain adaptation by backpropagation"
    - "deep cocktail network multi source unsupervised domain adaptation with category shift"
    - "progressive feature alignment for unsupervised domain adaptation"
    - "image to image translation for domain adaptation"
    - "deep hashing network for unsupervised domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "a sample selection approach for universal domain adaptation"):
  with members:
    - "pseudo labeling curriculum for unsupervised domain adaptation"
    - "open set domain adaptation for image and action recognition"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "partial adversarial domain adaptation"
    - "image to image translation for domain adaptation"
    - "deep hashing network for unsupervised domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "partial transfer learning with selective adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "transferability and hardness of supervised classification tasks"):
  with members:
    - "registration free face ssd single shot analysis of smiles facial attributes and affect in the wild"
    - "beyond sharing weights for deep domain adaptation"
    - "task2vec task embedding for meta learning"
    - "on measuring the iconicity of a face"
    - "the information complexity of learning tasks their structure and their distance"
    - "toward understanding catastrophic forgetting in continual learning"
    - "regularized learning for domain adaptation under label shifts"
    - "meta learning with latent embedding optimization"

Connected through hyperedge HE-5 (induced by source paper "looking back at labels a class based domain adaptation technique"):
  with members:
    - "adversarial adaptation of scene graph models for understanding civic issues"
    - "beyond sharing weights for deep domain adaptation"
    - "multimodal differential network for visual question generation"
    - "differential attention for visual question answering"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "partial adversarial domain adaptation"
    - "multi adversarial domain adaptation"
    - "learning semantic sentence embeddings using sequential pair wise discriminator"

Connected through hyperedge HE-6 (induced by source paper "alignflow cycle consistent learning from multiple domains via normalizing flows"):
  with members:
    - "domain adaptation with asymmetrically relaxed distribution alignment"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "video to video synthesis"
    - "neural autoregressive flows"
    - "universal neural machine translation for extremely low resource languages"
    - "multimodal unsupervised image to image translation"
    - "neural ordinary differential equations"
    - "generalization bounds for unsupervised cross domain mapping with wgans"

Connected through hyperedge HE-7 (induced by source paper "a plug in method for representation factorization"):
  with members:
    - "seeing what a gan cannot generate"
    - "wasserstein dependency measure for representation learning"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "generative adversarial networks gans what it can generate and what it cannot"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "neural voice cloning with a few samples"
    - "learning deep disentangled embeddings with the f statistic loss"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"

Connected through hyperedge HE-8 (induced by source paper "on direct distribution matching for adapting segmentation networks"):
  with members:
    - "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation"
    - "task driven generative modeling for unsupervised domain adaptation application to x ray image segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "synseg net synthetic segmentation without target modality ground truth"
    - "semantic aware generative adversarial nets for unsupervised domain adaptation in chest x ray segmentation"
    - "a novel domain adaptation framework for medical image segmentation"

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)