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 "unsupervised scene adaptation with memory regularization in vivo"):
  Abstract: We consider the unsupervised scene adaptation problem of learning from both labeled source data and unlabeled target data. Existing methods focus on minoring the inter-domain gap between the source and target domains. However, the intra-domain knowledge and inherent uncertainty learned by the network are under-explored. In this paper, we propose an orthogonal method, called memory regularization in vivo to exploit the intra-domain knowledge and regularize the model training. Specifically, we refer to the segmentation model itself as the memory module, and minor the discrepancy of the two classifiers, i.e., the primary classifier and the auxiliary classifier, to reduce the prediction inconsistency. Without extra parameters, the proposed method is complementary to the most existing domain adaptation methods and could generally improve the performance of existing methods. Albeit simple, we verify the effectiveness of memory regularization on two synthetic-to-real benchmarks: GTA5 -> Cityscapes and SYNTHIA -> Cityscapes, yielding +11.1% and +11.3% mIoU improvement over the baseline model, respectively. Besides, a similar +12.0% mIoU improvement is observed on the cross-city benchmark: Cityscapes -> Oxford RobotCar.

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 "dual graph convolutional network for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint 3d tracking and forecasting with graph neural network and diversity sampling")
  Member "confidence regularized self training":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation")
  Member "domain adaptation for structured output via discriminative patch representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adapting object detectors with conditional domain normalization")
  Member "regularizing proxies with multi adversarial training for unsupervised domain adaptive semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self training with progressive augmentation for unsupervised cross domain person re identification")
  Member "significance aware information bottleneck for domain adaptive semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "label driven reconstruction for domain adaptation in semantic segmentation")
  Member "manifold criterion guided transfer learning via intermediate domain generation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation")

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)