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 "transfer learning with dynamic distribution adaptation"):
  Abstract: Transfer learning aims to learn robust classifiers for the target domain by leveraging knowledge from a source domain. Since the source and the target domains are usually from different distributions, existing methods mainly focus on adapting the cross-domain marginal or conditional distributions. However, in real applications, the marginal and conditional distributions usually have different contributions to the domain discrepancy. Existing methods fail to quantitatively evaluate the different importance of these two distributions, which will result in unsatisfactory transfer performance. In this paper, we propose a novel concept called Dynamic Distribution Adaptation (DDA), which is capable of quantitatively evaluating the relative importance of each distribution. DDA can be easily incorporated into the framework of structural risk minimization to solve transfer learning problems. On the basis of DDA, we propose two novel learning algorithms: (1) Manifold Dynamic Distribution Adaptation (MDDA) for traditional transfer learning, and (2) Dynamic Distribution Adaptation Network (DDAN) for deep transfer learning. Extensive experiments demonstrate that MDDA and DDAN significantly improve the transfer learning performance and setup a strong baseline over the latest deep and adversarial methods on digits recognition, sentiment analysis, and image classification. More importantly, it is shown that marginal and conditional distributions have different contributions to the domain divergence, and our DDA is able to provide good quantitative evaluation of their relative importance which leads to better performance. We believe this observation can be helpful for future research in transfer learning.

Includes members:
  Member "fedhealth a federated transfer learning framework for wearable healthcare":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "privacy preserving traffic flow prediction a federated learning approach")
  Member "decomposition based transfer distance metric learning for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cost sensitive feature selection by optimizing f measures")
  Member "easy transfer learning by exploiting intra domain structures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transfer learning with dynamic adversarial adaptation network")
  Member "transfer learning from speaker verification to multispeaker text to speech synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parallel neural text to speech")
  Member "importance weighted adversarial nets for partial domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "visual domain adaptation with manifold embedded distribution alignment":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "impact of imagenet model selection on domain adaptation")
  Member "cross position activity recognition with stratified transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transfer learning with dynamic adversarial adaptation network")
  Member "multi adversarial domain adaptation":
    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.LG(Machine Learning)