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 "weakly supervised unconstrained action unit detection via feature disentanglement"):
  Abstract: Facial action unit (AU) detection in the wild is a challenging problem, with current methods either depending on impractical labor-intensive labeling by experts, or inaccurate pseudo labels. In this paper, we aim to exploit accurate AU labels from a well-constrained source domain for training an AU detector for the target domain of unconstrained in-the-wild images. Instead of attempting to map directly from source to target domains, we propose to generate a new feature domain to combine source-domain facial inner landmark features (denoted as shape features) with target-domain global pose and texture features (denoted as texture features), so as to train target AU detector with source AU labels. Specifically, we first disentangle the rich features learned from images into shape features and texture features by introducing a novel shape adversarial loss and a shape classification loss. After swapping the shape features of unpaired source and target images, the combined source shape features and target texture features are translated to the new domain by learning a mapping that maximizes the AU detector performance. A further disentanglement and swap is applied to cross-cyclically reconstruct the original rich features. Moreover, our framework can also be naturally extended for use with pseudo AU labels. Extensive experiments show that our method soundly outperforms the baseline, upper-bound methods and state-of-the-art approaches on the challenging benchmark dataset EmotioNet.

Includes members:
  Member "drit diverse image to image translation via disentangled representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "adversarial discriminative domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "regularizing face verification nets for pain intensity regression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep weakly supervised domain adaptation for pain localization in videos")
  Member "instance normalization the missing ingredient for fast stylization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on feature normalization and data augmentation")

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)