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: "deep domain adaptation by geodesic distance minimization"
  Abstract: In this paper, we propose a new approach called Deep LogCORAL for unsupervised visual domain adaptation. Our work builds on the recently proposed Deep CORAL method, which proposed to train a convolutional neural network and simultaneously minimize the Euclidean distance of convariance matrices between the source and target domains. We propose to use the Riemannian distance, approximated by Log-Euclidean distance, to replace the naive Euclidean distance in Deep CORAL. We also consider first-order information, and minimize the distance of mean vectors between two domains. We build an end-to-end model, in which we minimize both the classification loss, and the domain difference based on the first and second order information between two domains. Our experiments on the benchmark Office dataset demonstrate the improvements of our newly proposed Deep LogCORAL approach over the Deep CORAL method, as well as further improvement when optimizing both orders of information.

Connected through hyperedge HE-1 (induced by source paper "a robust learning approach to domain adaptive object detection"):
  with members:
    - "semantic foggy scene understanding with synthetic data"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "joint optimization framework for learning with noisy labels"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "advent adversarial entropy minimization for domain adaptation in semantic segmentation"
    - "image to image translation for domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "balancing domain gap for object instance detection"):
  with members:
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "multimodal unsupervised image to image translation"
    - "domain stylization a strong simple baseline for synthetic to real image domain adaptation"
    - "structured domain randomization bridging the reality gap by context aware synthetic data"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "autodial automatic domain alignment layers"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-3 (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-4 (induced by source paper "prototype rectification for few shot learning"):
  with members:
    - "a baseline for few shot image classification"
    - "meta learning with differentiable convex optimization"
    - "boosting few shot visual learning with self supervision"
    - "infinite mixture prototypes for few shot learning"
    - "edge labeling graph neural network for few shot learning"
    - "a closer look at few shot classification"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"

Connected through hyperedge HE-5 (induced by source paper "domain adaptive faster r cnn for object detection in the wild"):
  with members:
    - "semantic foggy scene understanding with synthetic data"
    - "road reality oriented adaptation for semantic segmentation of urban scenes"
    - "when unsupervised domain adaptation meets tensor representations"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unified deep supervised domain adaptation and generalization"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"):
  with members:
    - "adversarial discriminative domain adaptation"
    - "dilated residual networks"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "unsupervised domain adaptation in brain lesion segmentation with adversarial networks"
    - "deep coral correlation alignment for deep domain adaptation"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "zero shot deep domain adaptation"):
  with members:
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"
    - "deep hashing network for unsupervised domain adaptation"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "a compact dnn approaching googlenet level accuracy of classification and domain adaptation"
    - "deep multimodal representation learning from temporal data"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "addressing appearance change in outdoor robotics with adversarial domain adaptation"
    - "adversarial discriminative domain adaptation"

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)