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: "anti makeup learning a bi level adversarial network for makeup invariant face verification"
  Abstract: Makeup is widely used to improve facial attractiveness and is well accepted by the public. However, different makeup styles will result in significant facial appearance changes. It remains a challenging problem to match makeup and non-makeup face images. This paper proposes a learning from generation approach for makeup-invariant face verification by introducing a bi-level adversarial network (BLAN). To alleviate the negative effects from makeup, we first generate non-makeup images from makeup ones, and then use the synthesized non-makeup images for further verification. Two adversarial networks in BLAN are integrated in an end-to-end deep network, with the one on pixel level for reconstructing appealing facial images and the other on feature level for preserving identity information. These two networks jointly reduce the sensing gap between makeup and non-makeup images. Moreover, we make the generator well constrained by incorporating multiple perceptual losses. Experimental results on three benchmark makeup face datasets demonstrate that our method achieves state-of-the-art verification accuracy across makeup status and can produce photo-realistic non-makeup face images.

Connected through hyperedge HE-1 (induced by source paper "content and colour distillation for learning image translations with the spatial profile loss"):
  with members:
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "improving gan training via binarized representation entropy bre regularization"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the contextual loss for image transformation with non aligned data"
    - "which training methods for gans do actually converge"
    - "perceptual adversarial networks for image to image transformation"

Connected through hyperedge HE-2 (induced by source paper "conditional transferring features scaling gans to thousands of classes with 30 less high quality data for training"):
  with members:
    - "cgans with projection discriminator"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "unsupervised representation learning by predicting image rotations"
    - "progressive growing of gans for improved quality stability and variation"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "calibrating energy based generative adversarial networks"
    - "lr gan layered recursive generative adversarial networks for image generation"

Connected through hyperedge HE-3 (induced by source paper "attributes guided feature learning for vehicle re identification"):
  with members:
    - "pedestrian attribute recognition a survey"
    - "improving person re identification by attribute and identity learning"
    - "person re identification by camera correlation aware feature augmentation"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "person search with natural language description"
    - "camera style adaptation for person re identification"
    - "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"

Connected through hyperedge HE-4 (induced by source paper "style aggregated network for facial landmark detection"):
  with members:
    - "exploring disentangled feature representation beyond face identification"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "gans for biological image synthesis"
    - "camera style adaptation for person re identification"
    - "pose invariant face alignment with a single cnn"
    - "face aging with conditional generative adversarial networks"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"

Connected through hyperedge HE-5 (induced by source paper "fd gan face demorphing generative adversarial network for restoring accomplice s facial image"):
  with members:
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"

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)