human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "training deep face recognition systems with synthetic data"
  Abstract: Recent advances in deep learning have significantly increased the performance of face recognition systems. The performance and reliability of these models depend heavily on the amount and quality of the training data. However, the collection of annotated large datasets does not scale well and the control over the quality of the data decreases with the size of the dataset. In this work, we explore how synthetically generated data can be used to decrease the number of real-world images needed for training deep face recognition systems. In particular, we make use of a 3D morphable face model for the generation of images with arbitrary amounts of facial identities and with full control over image variations, such as pose, illumination, and background. In our experiments with an off-the-shelf face recognition software we observe the following phenomena: 1) The amount of real training data needed to train competitive deep face recognition systems can be reduced significantly. 2) Combining large-scale real-world data with synthetic data leads to an increased performance. 3) Models trained only on synthetic data with strong variations in pose, illumination, and background perform very well across different datasets even without dataset adaptation. 4) The real-to-virtual performance gap can be closed when using synthetic data for pre-training, followed by fine-tuning with real-world images. 5) There are no observable negative effects of pre-training with synthetic data. Thus, any face recognition system in our experiments benefits from using synthetic face images. The synthetic data generator, as well as all experiments, are publicly available.

Connected through hyperedge HE-1 (induced by source paper "cross domain image manipulation by demonstration"):
  with members:
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "neural best buddies sparse cross domain correspondence"
    - "multimodal unsupervised image to image translation"
    - "dida disentangled synthesis for domain adaptation"
    - "isolating sources of disentanglement in variational autoencoders"
    - "disentangling factors of variation with cycle consistent variational auto encoders"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "empirically analyzing the effect of dataset biases on deep face recognition systems"

Connected through hyperedge HE-2 (induced by source paper "generating photo realistic training data to improve face recognition accuracy"):
  with members:
    - "representation learning by rotating your faces"
    - "domain specific face synthesis for video face recognition from a single sample per person"
    - "privacy protective gan for face de identification"
    - "recent progress of face image synthesis"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "face aging with conditional generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "improving face detection performance with 3d rendered synthetic data"):
  with members:
    - "survey of face detection on low quality images"
    - "seeing small faces from robust anchor s perspective"
    - "pyramidbox a context assisted single shot face detector"
    - "ganimation anatomically aware facial animation from a single image"
    - "sface an efficient network for face detection in large scale variations"
    - "pushing the limits of unconstrained face detection a challenge dataset and baseline results"
    - "recent progress of face image synthesis"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"

Question: Please tell me which class the center node 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)