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 "can synthetic faces undo the damage of dataset bias to face recognition and facial landmark detection"):
  Abstract: It is well known that deep learning approaches to face recognition and facial landmark detection suffer from biases in modern training datasets. In this work, we propose to use synthetic face images to reduce the negative effects of dataset biases on these tasks. Using a 3D morphable face model, we generate large amounts of synthetic face images with full control over facial shape and color, pose, illumination, and background. With a series of experiments, we extensively test the effects of priming deep nets by pre-training them with synthetic faces. We observe the following positive effects for face recognition and facial landmark detection tasks: 1) Priming with synthetic face images improves the performance consistently across all benchmarks because it reduces the negative effects of biases in the training data. 2) Traditional approaches for reducing the damage of dataset bias, such as data augmentation and transfer learning, are less effective than training with synthetic faces. 3) Using synthetic data, we can reduce the size of real-world datasets by 75% for face recognition and by 50% for facial landmark detection while maintaining performance. Thus, offering a means to focus the data collection process on less but higher quality data.

Includes members:
  Member "virtual training for a real application accurate object robot relative localization without calibration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "large scale historical watermark recognition dataset and a new consistency based approach")
  Member "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "contemplating visual emotions understanding and overcoming dataset bias":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning de biased representations with biased representations")
  Member "what are the biases in my word embedding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mark my word a sequence to sequence approach to definition modeling")
  Member "sim4cv a photo realistic simulator for computer vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to train with synthetic humans")
  Member "robot learning in homes improving generalization and reducing dataset bias":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning predictive representations for deformable objects using contrastive estimation")
  Member "empirically analyzing the effect of dataset biases on deep face recognition systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge")
  Member "inversefacenet deep single shot inverse face rendering from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pushing the envelope for rgb based dense 3d hand pose estimation via neural rendering")

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)