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 "a balanced and uncertainty aware approach for partial domain adaptation"):
  Abstract: This work addresses the unsupervised domain adaptation problem, especially for the partial scenario where the class labels in the target domain are only a subset of those in the source domain. Such a partial transfer setting sounds realistic but challenging while existing methods always suffer from two key problems, i.e., negative transfer and uncertainty propagation. In this paper, we build on domain adversarial learning and propose a novel domain adaptation method BA$^3$US with two new techniques termed Balanced Adversarial Alignment (BAA) and Adaptive Uncertainty Suppression (AUS), respectively. On one hand, negative transfer results in that target samples are misclassified to the classes only present in the source domain. To address this issue, BAA aims to pursue the balance between label distributions across domains in a quite simple manner. Specifically, it randomly leverages a few source samples to augment the smaller target domain during domain alignment so that classes in different domains are symmetric. On the other hand, a source sample is denoted as uncertain if there is an incorrect class that has a relatively high prediction score. Such uncertainty is easily propagated to the unlabeled target data around it during alignment, which severely deteriorates the adaptation performance. Thus, AUS emphasizes uncertain samples and exploits an adaptive weighted complement entropy objective to expect that incorrect classes have the uniform and low prediction scores. Experimental results on multiple benchmarks demonstrate that BA$^3$US surpasses state-of-the-arts for partial domain adaptation tasks.

Includes members:
  Member "complement objective training":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "guess first to enable better compression and adversarial robustness")
  Member "tackling partial domain adaptation with self supervision":
    also connected through other hyperedges:
      (none)
  Member "learning to transfer examples 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 "virtual mixup training for unsupervised domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning smooth representation for unsupervised domain adaptation")
  Member "transfer adaptation learning a decade survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "semi supervised learning by augmented distribution alignment":
    also connected through other hyperedges:
      (none)
  Member "characterizing and avoiding negative transfer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "harmonizing transferability and discriminability for adapting object detectors")
  Member "importance weighted adversarial nets for partial domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)