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: "scut fbp a benchmark dataset for facial beauty perception"
  Abstract: In this paper, a novel face dataset with attractiveness ratings, namely, the SCUT-FBP dataset, is developed for automatic facial beauty perception. This dataset provides a benchmark to evaluate the performance of different methods for facial attractiveness prediction, including the state-of-the-art deep learning method. The SCUT-FBP dataset contains face portraits of 500 Asian female subjects with attractiveness ratings, all of which have been verified in terms of rating distribution, standard deviation, consistency, and self-consistency. Benchmark evaluations for facial attractiveness prediction were performed with different combinations of facial geometrical features and texture features using classical statistical learning methods and the deep learning method. The best Pearson correlation (0.8187) was achieved by the CNN model. Thus, the results of our experiments indicate that the SCUT-FBP dataset provides a reliable benchmark for facial beauty perception.

Connected through hyperedge HE-1 (induced by source paper "face beautification beyond makeup transfer"):
  with members:
    - "understanding beauty via deep facial features"
    - "multimodal unsupervised image to image translation"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "a style based generator architecture for generative adversarial networks"
    - "scut fbp5500 a diverse benchmark dataset for multi paradigm facial beauty prediction"
    - "drit diverse image to image translation via disentangled representations"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "robust conditional gan from uncertainty aware pairwise comparisons"):
  with members:
    - "deep robust subjective visual property prediction in crowdsourcing"
    - "high fidelity image generation with fewer labels"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "age progression regression by conditional adversarial autoencoder"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "training region based object detectors with online hard example mining"
    - "improved techniques for training gans"
    - "adversarial feature learning"

Connected through hyperedge HE-4 (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"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "a new humanlike facial attractiveness predictor with cascaded fine tuning deep learning model"

Connected through hyperedge HE-5 (induced by source paper "transferring rich deep features for facial beauty prediction"):
  with members:
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "a new humanlike facial attractiveness predictor with cascaded fine tuning deep learning model"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "deepethnic multi label ethnic classification from face images"):
  with members:
    - "learned features are better for ethnicity classification"
    - "rethinking the inception architecture for computer vision"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "scut fbp5500 a diverse benchmark dataset for multi paradigm facial beauty prediction"):
  with members:
    - "aggregated residual transformations for deep neural networks"
    - "deep residual learning for image recognition"
    - "the beauty of capturing faces rating the quality of digital portraits"

Connected through hyperedge HE-8 (induced by source paper "label distribution based facial attractiveness computation by deep residual learning"):
  with members:
    - "deep residual learning for image recognition"
    - "a new humanlike facial attractiveness predictor with cascaded fine tuning deep learning model"
    - "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)