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: "joint cross domain classification and subspace learning for unsupervised adaptation"
  Abstract: Domain adaptation aims at adapting the knowledge acquired on a source domain to a new different but related target domain. Several approaches have beenproposed for classification tasks in the unsupervised scenario, where no labeled target data are available. Most of the attention has been dedicated to searching a new domain-invariant representation, leaving the definition of the prediction function to a second stage. Here we propose to learn both jointly. Specifically we learn the source subspace that best matches the target subspace while at the same time minimizing a regularized misclassification loss. We provide an alternating optimization technique based on stochastic sub-gradient descent to solve the learning problem and we demonstrate its performance on several domain adaptation tasks.

Connected through hyperedge HE-1 (induced by source paper "a review of domain adaptation without target labels"):
  with members:
    - "regularized learning for domain adaptation under label shifts"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "sample to sample correspondence for unsupervised domain adaptation"
    - "unsupervised domain adaptation using regularized hyper graph matching"
    - "theoretical perspective of deep domain adaptation"
    - "deep visual domain adaptation a survey"
    - "characterizing and avoiding negative transfer"
    - "detecting and correcting for label shift with black box predictors"

Connected through hyperedge HE-2 (induced by source paper "beyond sharing weights for deep domain adaptation"):
  with members:
    - "progressive neural networks"
    - "adversarial discriminative domain adaptation"
    - "domain adaptation by mixture of alignments of second or higher order scatter tensors"
    - "deep reconstruction classification networks for unsupervised domain adaptation"
    - "cross stitch networks for multi task learning"
    - "correlation alignment for unsupervised domain adaptation"
    - "deep transfer learning with joint adaptation networks"
    - "domain separation networks"

Connected through hyperedge HE-3 (induced by source paper "learning to contextually aggregate multi source supervision for sequence labeling"):
  with members:
    - "crossweigh training named entity tagger from imperfect annotations"
    - "massively multilingual transfer for ner"
    - "multinomial adversarial networks for multi domain text classification"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "cross type biomedical named entity recognition with deep multi task learning"
    - "semi supervised sequence modeling with cross view training"
    - "neural adaptation layers for cross domain named entity recognition"
    - "asymmetric tri training for unsupervised domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "kernel transform learning"):
  with members:
    - "linearized kernel dictionary learning"
    - "efficient blind compressed sensing using sparsifying transforms with convergence guarantees and application to mri"

Connected through hyperedge HE-5 (induced by source paper "gotta adapt em all joint pixel and feature level domain adaptation for recognition in the wild"):
  with members:
    - "towards high fidelity nonlinear 3d face morphable model"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "nonlinear 3d face morphable model"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "cycada cycle consistent adversarial domain adaptation"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "good semi supervised learning that requires a bad gan"
    - "focal loss for dense object detection"

Connected through hyperedge HE-6 (induced by source paper "tracking persons of interest via unsupervised representation adaptation"):
  with members:
    - "person recognition in personal photo collections"
    - "yolov3 an incremental improvement"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "pixel level matching for video object segmentation using convolutional neural networks"
    - "adversarial discriminative domain adaptation"
    - "simple online and realtime tracking with a deep association metric"
    - "learning feed forward one shot learners"
    - "fully convolutional siamese networks for object tracking"

Connected through hyperedge HE-7 (induced by source paper "unsupervised domain adaptation for face recognition in unlabeled videos"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "neural aggregation network for video face recognition"
    - "unsupervised cross domain image generation"
    - "do we really need to collect millions of faces for effective face recognition"
    - "pixel level domain transfer"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "coupled generative adversarial networks"
    - "view synthesis by appearance flow"

Connected through hyperedge HE-8 (induced by source paper "fine grained recognition in the wild a multi task domain adaptation approach"):
  with members:
    - "pixel level domain transfer"
    - "lightweight unsupervised domain adaptation by convolutional filter reconstruction"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "multi way multilingual neural machine translation with a shared attention mechanism"
    - "domain separation networks"
    - "unsupervised domain adaptation with residual transfer networks"
    - "a large scale car dataset for fine grained categorization and verification"
    - "instance aware semantic segmentation via multi task network cascades"

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)