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: "quantifying facial age by posterior of age comparisons"
  Abstract: We introduce a novel approach for annotating large quantity of in-the-wild facial images with high-quality posterior age distribution as labels. Each posterior provides a probability distribution of estimated ages for a face. Our approach is motivated by observations that it is easier to distinguish who is the older of two people than to determine the person's actual age. Given a reference database with samples of known ages and a dataset to label, we can transfer reliable annotations from the former to the latter via human-in-the-loop comparisons. We show an effective way to transform such comparisons to posterior via fully-connected and SoftMax layers, so as to permit end-to-end training in a deep network. Thanks to the efficient and effective annotation approach, we collect a new large-scale facial age dataset, dubbed `MegaAge', which consists of 41,941 images. Data can be downloaded from our project page mmlab.ie.cuhk.edu.hk/projects/MegaAge and github.com/zyx2012/Age_estimation_BMVC2017. With the dataset, we train a network that jointly performs ordinal hyperplane classification and posterior distribution learning. Our approach achieves state-of-the-art results on popular benchmarks such as MORPH2, Adience, and the newly proposed MegaAge.

Connected through hyperedge HE-1 (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"
    - "random erasing data augmentation"
    - "deep label distribution learning with label ambiguity"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeeze and excitation networks"

Connected through hyperedge HE-2 (induced by source paper "uva a universal variational framework for continuous age analysis"):
  with members:
    - "global and local consistent wavelet domain age synthesis"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "global and local consistent age generative adversarial networks"
    - "disentangled variational representation for heterogeneous face recognition"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "learning face age progression a pyramid architecture of gans"

Connected through hyperedge HE-3 (induced by source paper "reenactgan learning to reenact faces via boundary transfer"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "hallucinated iqa no reference image quality assessment via adversarial learning"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "densereg fully convolutional dense shape regression in the wild"
    - "style aggregated network for facial landmark detection"
    - "densepose dense human pose estimation in the wild"
    - "deep structured learning for facial action unit intensity estimation"
    - "be your own prada fashion synthesis with structural coherence"

Connected through hyperedge HE-4 (induced by source paper "a coupled evolutionary network for age estimation"):
  with members:
    - "deep label distribution learning with label ambiguity"
    - "deep regression forests for age estimation"
    - "apparent age estimation using ensemble of deep learning models"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "deep residual learning for image recognition"
    - "a light cnn for deep face representation with noisy labels"
    - "label distribution learning"

Connected through hyperedge HE-5 (induced by source paper "image ordinal classification and understanding grid dropout with masking label"):
  with members:
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "visualizing and understanding convolutional networks"
    - "improving neural networks by preventing co adaptation of feature detectors"

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)