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 "selective transfer with reinforced transfer network for partial domain adaptation"):
  Abstract: Partial domain adaptation (PDA) extends standard domain adaptation to a more realistic scenario where the target domain only has a subset of classes from the source domain. The key challenge of PDA is how to select the relevant samples in the shared classes for knowledge transfer. Previous PDA methods tackle this problem by re-weighting the source samples based on the prediction of classifier or discriminator, thus discarding the pixel-level information. In this paper, to utilize both high-level and pixel-level information, we propose a reinforced transfer network (RTNet), which is the first work to apply reinforcement learning to address the PDA problem. The RTNet simultaneously mitigates the negative transfer by adopting a reinforced data selector to filter out outlier source classes, and promotes the positive transfer by employing a domain adaptation model to minimize the distribution discrepancy in the shared label space. Extensive experiments indicate that RTNet can achieve state-of-the-art performance for partial domain adaptation tasks on several benchmark datasets. Codes and datasets will be available online.

Includes members:
  Member "homm higher order moment matching for unsupervised domain adaptation":
    also connected through other hyperedges:
      (none)
  Member "learning to transfer examples for partial domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "learning to selectively transfer reinforced transfer learning for deep text matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "selective weak supervision for neural information retrieval")
  Member "reinforced co training":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey towards federated semi supervised learning")
  Member "importance weighted adversarial nets for partial domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "learning sampling policies for domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to selectively transfer reinforced transfer learning for deep text matching")
  Member "joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "impact of imagenet model selection on domain adaptation")

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)