human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "open set domain adaptation by backpropagation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "importance weighted adversarial nets for partial domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "partial adversarial domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "deep hashing network for unsupervised domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "revisiting meta learning as supervised learning")
  Member "cycada cycle consistent adversarial domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a novel framework for selection of gans for an application")
  Member "partial transfer learning with selective adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "adversarial discriminative domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "deep transfer learning with joint adaptation networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")

Question: Please tell me which class the source paper 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)