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: "understanding beauty via deep facial features"
  Abstract: The concept of beauty has been debated by philosophists and psychologists for centuries, but most definitions are subjective and metaphysical, and deficit in accuracy, generality, and scalability. In this paper, we present a novel study on mining beauty semantics of facial attributes based on big data, with an attempt to objectively construct descriptions of beauty in a quantitative manner. We first deploy a deep convolutional neural network (CNN) to extract facial attributes, and then investigate correlations between these features and attractiveness on two large-scale datasets labelled with beauty scores. Not only do we discover the secrets of beauty verified by statistical significance tests, our findings also align perfectly with existing psychological studies that, e.g., small nose, high cheekbones, and femininity contribute to attractiveness. We further leverage these high-level representations to original images by a generative adversarial network (GAN). Beauty enhancements after synthesis are visually compelling and statistically convincing verified by a user survey of 10,000 data points.

Connected through hyperedge HE-1 (induced by source paper "anonymousnet natural face de identification with measurable privacy"):
  with members:
    - "beauty learning and counterfactual inference"
    - "semi supervised text regression with conditional generative adversarial networks"
    - "privacy protective gan for face de identification"
    - "technical privacy metrics a systematic survey"
    - "privacy preserving machine learning through data obfuscation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "style aggregated network for facial landmark detection"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "face beautification beyond makeup transfer"):
  with members:
    - "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"
    - "semantically decomposing the latent spaces of generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "beauty learning and counterfactual inference"):
  with members:
    - "a style based generator architecture for generative adversarial networks"
    - "explaining neural networks semantically and quantitatively"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "going deeper with convolutions"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-4 (induced by source paper "music sequence prediction with mixture hidden markov models"):
  with members:
    - "anonymousnet natural face de identification with measurable privacy"
    - "component attention guided face super resolution network cagface"
    - "multiclass information flow propagation control under vehicle to vehicle communication environments"
    - "cooperative adaptive cruise control for connected autonomous vehicles by factoring communication related constraints"
    - "youtube av 50k an annotated corpus for comments in autonomous vehicles"
    - "cooperative adaptive cruise control for a platoon of connected and autonomous vehicles considering dynamic information flow topology"
    - "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"
    - "the similarity metric"

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)