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 "semi supervised unconstrained action unit detection via latent feature domain"):
  Abstract: Facial action unit (AU) detection in the wild is a challenging problem, due to the unconstrained variability in facial appearances and the lack of accurate AU annotations. Most existing methods either depend on impractical labor-intensive labeling by experts, or inaccurate pseudo labels. In this paper, we propose an end-to-end semi-supervised unconstrained AU detection framework, which transfers accurate AU labels from a constrained source domain to an unconstrained target domain by exploiting accurate labels of AU-related facial landmarks. Specifically, we map a source image with AU label and a target image without AU label into a latent feature domain by combining source landmark-related feature with target landmark-free feature. Due to the combination of source AU-related information and target AU-free information, the latent feature domain with the transferred source AU label can be learned by maximizing the target-domain AU detection performance. Moreover, to disentangle landmark-related and landmark-free features, we introduce a novel landmark adversarial loss which can solve the multi-player minimax game in adversarial learning. Our framework can also be naturally extended for use with target-domain pseudo AU labels. Extensive experiments show that our method soundly outperforms the baselines, upper-bounds and state-of-the-art approaches on the challenging BP4D, GFT and EmotioNet benchmarks. The code for our method is available at https://github.com/ZhiwenShao/ADLD.

Includes members:
  Member "look at boundary a boundary aware face alignment algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "kpnet towards minimal face detector")
  Member "deep adaptive attention for joint facial action unit detection and face alignment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "region based adversarial synthesis of facial action units")
  Member "self supervised learning of a facial attribute embedding from video":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "t2net synthetic to realistic translation for solving single image depth estimation tasks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "crdoco pixel level domain transfer with cross domain consistency")
  Member "facial action unit detection using attention and relation learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "jaa net joint facial action unit detection and face alignment via adaptive attention")
  Member "residual parameter transfer for deep domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to adapt class specific features across domains for semantic segmentation")
  Member "adversarial discriminative domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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)