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: "learning to transfer examples for partial domain adaptation"
  Abstract: Domain adaptation is critical for learning in new and unseen environments. With domain adversarial training, deep networks can learn disentangled and transferable features that effectively diminish the dataset shift between the source and target domains for knowledge transfer. In the era of Big Data, the ready availability of large-scale labeled datasets has stimulated wide interest in partial domain adaptation (PDA), which transfers a recognizer from a labeled large domain to an unlabeled small domain. It extends standard domain adaptation to the scenario where target labels are only a subset of source labels. Under the condition that target labels are unknown, the key challenge of PDA is how to transfer relevant examples in the shared classes to promote positive transfer, and ignore irrelevant ones in the specific classes to mitigate negative transfer. In this work, we propose a unified approach to PDA, Example Transfer Network (ETN), which jointly learns domain-invariant representations across the source and target domains, and a progressive weighting scheme that quantifies the transferability of source examples while controlling their importance to the learning task in the target domain. A thorough evaluation on several benchmark datasets shows that our approach achieves state-of-the-art results for partial domain adaptation tasks.

Connected through hyperedge HE-1 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "orthogonal deep neural networks"
    - "unsupervised domain adaptation via regularized conditional alignment"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "beyond sharing weights for deep domain adaptation"
    - "domain symmetric networks for adversarial domain adaptation"
    - "bridging theory and algorithm for domain adaptation"
    - "open set domain adaptation for image and action recognition"

Connected through hyperedge HE-2 (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"
    - "cluster alignment with a teacher for unsupervised domain adaptation"
    - "federated adversarial domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "a balanced and uncertainty aware approach for partial domain adaptation"):
  with members:
    - "complement objective training"
    - "tackling partial domain adaptation with self supervision"
    - "virtual mixup training for unsupervised domain adaptation"
    - "transfer adaptation learning a decade survey"
    - "semi supervised learning by augmented distribution alignment"
    - "characterizing and avoiding negative transfer"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "partial adversarial domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "class conditional alignment for partial domain adaptation"):
  with members:
    - "deep visual domain adaptation a survey"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "partial adversarial domain adaptation"
    - "multi adversarial domain adaptation"
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"
    - "autodial automatic domain alignment layers"
    - "deep hashing network for unsupervised domain adaptation"
    - "partial transfer learning with selective adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "domain adaptation with conditional distribution matching and generalized label shift"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "domain agnostic learning with disentangled representations"
    - "robust natural language inference models with example forgetting"
    - "batch weight for domain adaptation with mass shift"
    - "learning representations by maximizing mutual information across views"
    - "regularized learning for domain adaptation under label shifts"
    - "on learning invariant representation for domain adaptation"
    - "multi source domain adaptation with mixture of experts"

Connected through hyperedge HE-6 (induced by source paper "universal domain adaptation through self supervision"):
  with members:
    - "unsupervised deep learning by neighbourhood discovery"
    - "momentum contrast for unsupervised visual representation learning"
    - "domain specific batch normalization for unsupervised domain adaptation"
    - "domain generalization by solving jigsaw puzzles"
    - "bridging theory and algorithm for domain adaptation"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "semi supervised domain adaptation via minimax entropy"
    - "cluster alignment with a teacher for unsupervised domain adaptation"

Connected through hyperedge HE-7 (induced by source paper "partially shared variational auto encoders for unsupervised domain adaptation with target shift"):
  with members:
    - "m adda unsupervised domain adaptation with deep metric learning"
    - "cluster alignment with a teacher for unsupervised domain adaptation"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "partial adversarial domain adaptation"
    - "spectral normalization for generative adversarial networks"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"
    - "drit diverse image to image translation via disentangled representations"

Connected through hyperedge HE-8 (induced by source paper "less confusion more transferable minimum class confusion for versatile domain adaptation"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "drop to adapt learning discriminative features for unsupervised domain adaptation"
    - "domain agnostic learning with disentangled representations"
    - "confidence regularized self training"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "bridging theory and algorithm for domain adaptation"
    - "transferrable prototypical networks for unsupervised domain adaptation"
    - "on learning invariant representation for 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)