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: "some like it hot visual guidance for preference prediction"
  Abstract: For people first impressions of someone are of determining importance. They are hard to alter through further information. This begs the question if a computer can reach the same judgement. Earlier research has already pointed out that age, gender, and average attractiveness can be estimated with reasonable precision. We improve the state-of-the-art, but also predict - based on someone's known preferences - how much that particular person is attracted to a novel face. Our computational pipeline comprises a face detector, convolutional neural networks for the extraction of deep features, standard support vector regression for gender, age and facial beauty, and - as the main novelties - visual regularized collaborative filtering to infer inter-person preferences as well as a novel regression technique for handling visual queries without rating history. We validate the method using a very large dataset from a dating site as well as images from celebrities. Our experiments yield convincing results, i.e. we predict 76% of the ratings correctly solely based on an image, and reveal some sociologically relevant conclusions. We also validate our collaborative filtering solution on the standard MovieLens rating dataset, augmented with movie posters, to predict an individual's movie rating. We demonstrate our algorithms on howhot.io which went viral around the Internet with more than 50 million pictures evaluated in the first month.

Connected through hyperedge HE-1 (induced by source paper "learning preference based similarities from face images using siamese multi task cnns"):
  with members:
    - "scut fbp5500 a diverse benchmark dataset for multi paradigm facial beauty prediction"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "robust regression via deep negative correlation learning"):
  with members:
    - "residual non local attention networks for image restoration"
    - "bag of tricks for image classification with convolutional neural networks"
    - "first impressions a survey on vision based apparent personality trait analysis"
    - "explaining first impressions modeling recognizing and explaining apparent personality from videos"
    - "residual dense network for image super resolution"
    - "image super resolution via dual state recurrent networks"
    - "switching convolutional neural network for crowd counting"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"

Connected through hyperedge HE-3 (induced by source paper "c3ae exploring the limits of compact model for age estimation"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "facial aging and rejuvenation by conditional multi adversarial autoencoder with ordinal regression"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "quantifying facial age by posterior of age comparisons"
    - "random erasing data augmentation"
    - "deep label distribution learning with label ambiguity"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-4 (induced by source paper "facial age estimation by deep residual decision making"):
  with members:
    - "hierarchical binary cnns for landmark localization with limited resources"
    - "visualizing the decision making process in deep neural decision forest"
    - "interpreting cnns via decision trees"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "visualizing the loss landscape of neural nets"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "label distribution learning forests"
    - "interpretable convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "deep differentiable random forests for age estimation"):
  with members:
    - "deep label distribution learning with label ambiguity"
    - "label distribution learning forests"
    - "deep regression forests for age estimation"
    - "decision forests convolutional networks and the models in between"
    - "label distribution learning"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-6 (induced by source paper "personalised aesthetics with residual adapters"):
  with members:
    - "fast perceptual image enhancement"
    - "aesthetic based clothing recommendation"
    - "quantitative analysis of automatic image cropping algorithms a dataset and comparative study"
    - "image aesthetic assessment an experimental survey"
    - "learning multiple visual domains with residual adapters"
    - "deep cropping via attention box prediction and aesthetics assessment"

Connected through hyperedge HE-7 (induced by source paper "fine grained age estimation in the wild with attention lstm networks"):
  with members:
    - "residual networks of residual networks multilevel residual networks"
    - "deep label distribution learning with label ambiguity"
    - "age group and gender estimation in the wild with deep ror architecture"
    - "video summarization with attention based encoder decoder networks"
    - "dager deep age gender and emotion recognition using convolutional neural network"
    - "pyramidal ror for image classification"
    - "apparent age estimation using ensemble of deep learning models"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-8 (induced by source paper "classifying online dating profiles on tinder using facenet facial embeddings"):
  with members:
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "facenet a unified embedding for face recognition and clustering"

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)