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 fbp5500 a diverse benchmark dataset for multi paradigm facial beauty prediction"
  Abstract: Facial beauty prediction (FBP) is a significant visual recognition problem to make assessment of facial attractiveness that is consistent to human perception. To tackle this problem, various data-driven models, especially state-of-the-art deep learning techniques, were introduced, and benchmark dataset become one of the essential elements to achieve FBP. Previous works have formulated the recognition of facial beauty as a specific supervised learning problem of classification, regression or ranking, which indicates that FBP is intrinsically a computation problem with multiple paradigms. However, most of FBP benchmark datasets were built under specific computation constrains, which limits the performance and flexibility of the computational model trained on the dataset. In this paper, we argue that FBP is a multi-paradigm computation problem, and propose a new diverse benchmark dataset, called SCUT-FBP5500, to achieve multi-paradigm facial beauty prediction. The SCUT-FBP5500 dataset has totally 5500 frontal faces with diverse properties (male/female, Asian/Caucasian, ages) and diverse labels (face landmarks, beauty scores within [1,~5], beauty score distribution), which allows different computational models with different FBP paradigms, such as appearance-based/shape-based facial beauty classification/regression model for male/female of Asian/Caucasian. We evaluated the SCUT-FBP5500 dataset for FBP using different combinations of feature and predictor, and various deep learning methods. The results indicates the improvement of FBP and the potential applications based on the SCUT-FBP5500.

Connected through hyperedge HE-1 (induced by source paper "learning preference based similarities from face images using siamese multi task cnns"):
  with members:
    - "smoothgrad removing noise by adding noise"
    - "an overview of multi task learning in deep neural networks"
    - "afif4 deep gender classification based on adaboost based fusion of isolated facial features and foggy faces"
    - "faceboxes a cpu real time face detector with high accuracy"
    - "cross stitch networks for multi task learning"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "densely connected convolutional networks"
    - "some like it hot visual guidance for preference prediction"

Connected through hyperedge HE-2 (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"
    - "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 "unpaired image enhancement featuring reinforcement learning controlled image editing software"):
  with members:
    - "facelet bank for fast portrait manipulation"
    - "fully convolutional network with multi step reinforcement learning for image processing"
    - "distort and recover color enhancement using deep reinforcement learning"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "attention aware face hallucination via deep reinforcement learning"
    - "deep bilateral learning for real time image enhancement"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-4 (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"
    - "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"
    - "stargan unified generative adversarial networks for multi domain image to image translation"

Connected through hyperedge HE-5 (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"
    - "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"
    - "a new humanlike facial attractiveness predictor with cascaded fine tuning deep learning model"

Connected through hyperedge HE-6 (induced by source paper "on the effect of observed subject biases in apparent personality analysis from audio visual signals"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "investigating audio visual and text fusion methods for end to end automatic personality prediction"
    - "first impressions a survey on vision based apparent personality trait analysis"
    - "explaining first impressions modeling recognizing and explaining apparent personality from videos"
    - "age progression regression by conditional adversarial autoencoder"
    - "heterogeneous face attribute estimation a deep multi task learning approach"
    - "bi modal first impressions recognition using temporally ordered deep audio and stochastic visual features"
    - "deep impression audiovisual deep residual networks for multimodal apparent personality trait recognition"

Connected through hyperedge HE-7 (induced by source paper "xcloud design and implementation of ai cloud platform with restful api service"):
  with members:
    - "data driven approach for quality evaluation on knowledge sharing platform"
    - "sphereface deep hypersphere embedding for face recognition"
    - "visual search at ebay"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)