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 "rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation"):
  Abstract: This paper focuses on the unsupervised domain adaptation of transferring the knowledge from the source domain to the target domain in the context of semantic segmentation. Existing approaches usually regard the pseudo label as the ground truth to fully exploit the unlabeled target-domain data. Yet the pseudo labels of the target-domain data are usually predicted by the model trained on the source domain. Thus, the generated labels inevitably contain the incorrect prediction due to the discrepancy between the training domain and the test domain, which could be transferred to the final adapted model and largely compromises the training process. To overcome the problem, this paper proposes to explicitly estimate the prediction uncertainty during training to rectify the pseudo label learning for unsupervised semantic segmentation adaptation. Given the input image, the model outputs the semantic segmentation prediction as well as the uncertainty of the prediction. Specifically, we model the uncertainty via the prediction variance and involve the uncertainty into the optimization objective. To verify the effectiveness of the proposed method, we evaluate the proposed method on two prevalent synthetic-to-real semantic segmentation benchmarks, i.e., GTA5 -> Cityscapes and SYNTHIA -> Cityscapes, as well as one cross-city benchmark, i.e., Cityscapes -> Oxford RobotCar. We demonstrate through extensive experiments that the proposed approach (1) dynamically sets different confidence thresholds according to the prediction variance, (2) rectifies the learning from noisy pseudo labels, and (3) achieves significant improvements over the conventional pseudo label learning and yields competitive performance on all three benchmarks.

Includes members:
  Member "domain randomization and pyramid consistency simulation to real generalization without accessing target domain data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")
  Member "unsupervised domain adaptation via calibrating uncertainties":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "confidence regularized self training")
  Member "dual graph convolutional network for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint 3d tracking and forecasting with graph neural network and diversity sampling")
  Member "unsupervised scene adaptation with memory regularization in vivo":
    also connected through other hyperedges:
      (none)
  Member "confidence regularized self training":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cross domain self supervised learning for domain adaptation with few source labels")
  Member "an adversarial perturbation oriented domain adaptation approach for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "heatnet bridging the day night domain gap in semantic segmentation with thermal images")
  Member "benchmarking neural network robustness to common corruptions and perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet")
  Member "domain adaptation for structured output via discriminative patch representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adapting object detectors with conditional domain normalization")

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)