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: "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"
  Abstract: In computer vision, one is often confronted with problems of domain shifts, which occur when one applies a classifier trained on a source dataset to target data sharing similar characteristics (e.g. same classes), but also different latent data structures (e.g. different acquisition conditions). In such a situation, the model will perform poorly on the new data, since the classifier is specialized to recognize visual cues specific to the source domain. In this work we explore a solution, named DeepJDOT, to tackle this problem: through a measure of discrepancy on joint deep representations/labels based on optimal transport, we not only learn new data representations aligned between the source and target domain, but also simultaneously preserve the discriminative information used by the classifier. We applied DeepJDOT to a series of visual recognition tasks, where it compares favorably against state-of-the-art deep domain adaptation methods.

Connected through hyperedge HE-1 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation"):
  with members:
    - "drop to adapt learning discriminative features for unsupervised domain adaptation"
    - "domain agnostic learning with disentangled representations"
    - "towards federated learning at scale system design"
    - "when does label smoothing help"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "pseudo labeling curriculum for unsupervised domain adaptation"
    - "learning to transfer examples for partial domain adaptation"
    - "cluster alignment with a teacher for unsupervised domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "domain adaptive ensemble learning"):
  with members:
    - "fixmatch simplifying semi supervised learning with consistency and confidence"
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "episodic training for domain generalization"
    - "consistency regularization for generative adversarial networks"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "randaugment practical data augmentation with no separate search"
    - "domain generalization by solving jigsaw puzzles"
    - "semi supervised domain adaptation via minimax entropy"

Connected through hyperedge HE-3 (induced by source paper "cross domain self supervised learning for domain adaptation with few source labels"):
  with members:
    - "universal domain adaptation through self supervision"
    - "drop to adapt learning discriminative features for unsupervised domain adaptation"
    - "unsupervised deep learning by neighbourhood discovery"
    - "confidence regularized self training"
    - "momentum contrast for unsupervised visual representation learning"
    - "domain generalization by solving jigsaw puzzles"
    - "bridging theory and algorithm for domain adaptation"
    - "local aggregation for unsupervised learning of visual embeddings"

Connected through hyperedge HE-4 (induced by source paper "opposite structure learning for semi supervised domain adaptation"):
  with members:
    - "semi supervised domain adaptation via minimax entropy"
    - "moment matching for multi source domain adaptation"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "deep hashing network for unsupervised domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "few shot adversarial domain adaptation"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "large scale optimal transport via adversarial training with cycle consistency"):
  with members:
    - "on scalable and efficient computation of large scale optimal transport"
    - "adversarial computation of optimal transport maps"
    - "optimal transport mapping via input convex neural networks"
    - "scalable unbalanced optimal transport using generative adversarial networks"
    - "guiding the one to one mapping in cyclegan via optimal transport"
    - "cycada cycle consistent adversarial domain adaptation"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "incremental unsupervised domain adversarial training of neural networks"):
  with members:
    - "drop to adapt learning discriminative features for unsupervised domain adaptation"
    - "semi supervised domain adaptation via minimax entropy"
    - "a review of domain adaptation without target labels"
    - "deep visual domain adaptation a survey"
    - "adversarial discriminative domain adaptation"
    - "associative domain adaptation"
    - "deep reconstruction classification networks for unsupervised domain adaptation"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "multi marginal optimal transport defines a generalized metric"):
  with members:
    - "multi marginal wasserstein gan"
    - "a point set generation network for 3d object reconstruction from a single image"

Connected through hyperedge HE-8 (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"

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)