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: "a new humanlike facial attractiveness predictor with cascaded fine tuning deep learning model"
  Abstract: This paper proposes a deep leaning method to address the challenging facial attractiveness prediction problem. The method constructs a convolutional neural network of facial beauty prediction using a new deep cascaded fine-turning scheme with various face inputting channels, such as the original RGB face image, the detail layer image, and the lighting layer image. With a carefully designed CNN model of deep structure, large input size and small convolutional kernels, we have achieved a high prediction correlation of 0.88. This result convinces us that the problem of facial attractiveness prediction can be solved by deep learning approach, and it also shows the important roles of the facial smoothness, lightness, and color information that were involved in facial beauty perception, which is consistent with the result of recent psychology studies. Furthermore, we analyze the high-level features learnt by CNN through visualization of its hidden layers, and some interesting phenomena were observed. It is found that the contours and appearance of facial features, especially eyes and moth, are the most significant facial attributes for facial attractiveness prediction, which is also consistent with the visual perception intuition of human.

Connected through hyperedge HE-1 (induced by source paper "beholder gan generation and beautification of facial images with conditioning on their beauty level"):
  with members:
    - "ganimation anatomically aware facial animation from a single image"
    - "transferring rich deep features for facial beauty prediction"
    - "scut fbp5500 a diverse benchmark dataset for multi paradigm facial beauty prediction"
    - "progressive growing of gans for improved quality stability and variation"
    - "face aging with conditional generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-2 (induced by source paper "photofeeler d3 a neural network with voter modeling for dating photo impression prediction"):
  with members:
    - "transferring rich deep features for facial beauty prediction"
    - "scut fbp5500 a diverse benchmark dataset for multi paradigm facial beauty prediction"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "xception deep learning with depthwise separable convolutions"
    - "neural architecture search with reinforcement learning"
    - "scut fbp a benchmark dataset for facial beauty perception"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "transferring rich deep features for facial beauty prediction"):
  with members:
    - "scut fbp a benchmark dataset for facial beauty perception"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "3d face synthesis driven by personality impression"):
  with members:
    - "photorealistic facial texture inference using deep neural networks"
    - "going deeper with convolutions"
    - "learning face representation from scratch"

Connected through hyperedge HE-5 (induced by source paper "label distribution based facial attractiveness computation by deep residual learning"):
  with members:
    - "scut fbp a benchmark dataset for facial beauty perception"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

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)