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 "auglabel exploiting word representations to augment labels for face attribute classification"):
  Abstract: Augmenting data in image space (eg. flipping, cropping etc) and activation space (eg. dropout) are being widely used to regularise deep neural networks and have been successfully applied on several computer vision tasks. Unlike previous works, which are mostly focused on doing augmentation in the aforementioned domains, we propose to do augmentation in label space. In this paper, we present a novel method to generate fixed dimensional labels with continuous values for images by exploiting the word2vec representations of the existing categorical labels. We then append these representations with existing categorical labels and train the model. We validated our idea on two challenging face attribute classification data sets viz. CelebA and LFWA. Our extensive experiments show that the augmented labels improve the performance of the competitive deep learning baseline and reduce the need of annotated real data up to 50%, while attaining a performance similar to the state-of-the-art methods.

Includes members:
  Member "semi supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "inducing optimal attribute representations for conditional gans")
  Member "augmented skeleton space transfer for depth based hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation")
  Member "improving facial attribute prediction using semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "boosting deep face recognition via disentangling appearance and geometry")
  Member "regularizing neural networks by penalizing confident output distributions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised domain adaptive object detection using forward backward cyclic adaptation")
  Member "disturblabel regularizing cnn on the loss layer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "being bayesian about categorical probability")
  Member "cp mtml coupled projection multi task metric learning for large scale face retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sampling strategies for gan synthetic data")
  Member "walk and learn facial attribute representation learning from egocentric video and contextual data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "branched multi task networks deciding what layers to share")
  Member "ask your neurons a deep learning approach to visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "visual grounding in video for unsupervised word translation")

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)