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: "facial feature point detection a comprehensive survey"
  Abstract: This paper presents a comprehensive survey of facial feature point detection with the assistance of abundant manually labeled images. Facial feature point detection favors many applications such as face recognition, animation, tracking, hallucination, expression analysis and 3D face modeling. Existing methods can be categorized into the following four groups: constrained local model (CLM)-based, active appearance model (AAM)-based, regression-based, and other methods. CLM-based methods consist of a shape model and a number of local experts, each of which is utilized to detect a facial feature point. AAM-based methods fit a shape model to an image by minimizing texture synthesis errors. Regression-based methods directly learn a mapping function from facial image appearance to facial feature points. Besides the above three major categories of methods, there are also minor categories of methods which we classify into other methods: graphical model-based methods, joint face alignment methods, independent facial feature point detectors, and deep learning-based methods. Though significant progress has been made, facial feature point detection is limited in its success by wild and real-world conditions: variations across poses, expressions, illuminations, and occlusions. A comparative illustration and analysis of representative methods provide us a holistic understanding and deep insight into facial feature point detection, which also motivates us to explore promising future directions.

Connected through hyperedge HE-1 (induced by source paper "domain embedded multi model generative adversarial networks for image based face inpainting"):
  with members:
    - "image inpainting with learnable bidirectional attention maps"
    - "deep fusion network for image completion"
    - "learning pyramid context encoder network for high quality image inpainting"
    - "structureflow image inpainting via structure aware appearance flow"
    - "foreground aware image inpainting"
    - "pluralistic image completion"
    - "alphagan generative adversarial networks for natural image matting"
    - "semantic image inpainting through improved wasserstein generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "pfld a practical facial landmark detector"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "exploring disentangled feature representation beyond face identification"
    - "style aggregated network for facial landmark detection"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "improving landmark localization with semi supervised learning"

Connected through hyperedge HE-3 (induced by source paper "pose invariant 3d face reconstruction"):
  with members:
    - "wing loss for robust facial landmark localisation with convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"):
  with members:
    - "densereg fully convolutional dense shape regression in the wild"
    - "real time 3d face fitting and texture fusion on in the wild videos"
    - "face alignment in the wild a survey"
    - "facial performance capture with deep neural networks"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "production level facial performance capture using deep convolutional neural networks"
    - "deep cascaded bi network for face hallucination"
    - "robust lstm autoencoders for face de occlusion in the wild"

Connected through hyperedge HE-5 (induced by source paper "self supervised multi level face model learning for monocular reconstruction at over 250 hz"):
  with members:
    - "densereg fully convolutional dense shape regression in the wild"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "3d morphable models as spatial transformer networks"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "neural face editing with intrinsic image disentangling"
    - "real time 3d face fitting and texture fusion on in the wild videos"
    - "inversefacenet deep single shot inverse face rendering from a single image"
    - "face alignment in the wild a survey"

Connected through hyperedge HE-6 (induced by source paper "large scale sleep condition analysis using selfies from social media"):
  with members:
    - "using social media to promote stem education matching college students with role models"
    - "the effect of pets on happiness a data driven approach via large scale social media"

Connected through hyperedge HE-7 (induced by source paper "face alignment in the wild a survey"):
  with members:
    - "learning deep representation for face alignment with auxiliary attributes"
    - "two stage convolutional part heatmap regression for the 1st 3d face alignment in the wild 3dfaw challenge"
    - "deep residual learning for image recognition"
    - "articulated pose estimation using hierarchical exemplar based models"
    - "pose invariant 3d face alignment"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "an empirical study of recent face alignment methods"
    - "coarse to fine face alignment with multi scale local patch regression"

Connected through hyperedge HE-8 (induced by source paper "convolutional experts constrained local model for facial landmark detection"):
  with members:
    - "a comprehensive performance evaluation of deformable face tracking in the wild"
    - "tensor fusion network for multimodal sentiment analysis"
    - "mosi multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos"
    - "visualizing and understanding curriculum learning for long short term memory networks"
    - "cascaded continuous regression for real time incremental face tracking"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

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)