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: "deep recurrent regression for facial landmark detection"
  Abstract: We propose a novel end-to-end deep architecture for face landmark detection, based on a deep convolutional and deconvolutional network followed by carefully designed recurrent network structures. The pipeline of this architecture consists of three parts. Through the first part, we encode an input face image to resolution-preserved deconvolutional feature maps via a deep network with stacked convolutional and deconvolutional layers. Then, in the second part, we estimate the initial coordinates of the facial key points by an additional convolutional layer on top of these deconvolutional feature maps. In the last part, by using the deconvolutional feature maps and the initial facial key points as input, we refine the coordinates of the facial key points by a recurrent network that consists of multiple Long-Short Term Memory (LSTM) components. Extensive evaluations on several benchmark datasets show that the proposed deep architecture has superior performance against the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "recurrent convolutional strategies for face manipulation detection in videos"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "aird adversarial learning framework for image repurposing detection"
    - "mesonet a compact facial video forgery detection network"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "two stream neural networks for tampered face detection"
    - "deep multimodal image repurposing detection"
    - "action tubelet detector for spatio temporal action localization"

Connected through hyperedge HE-2 (induced by source paper "a high efficiency framework for constructing large scale face parsing benchmark"):
  with members:
    - "devil in the details towards accurate single and multiple human parsing"
    - "look at boundary a boundary aware face alignment algorithm"
    - "style aggregated network for facial landmark detection"
    - "synthesis of high quality visible faces from polarimetric thermal faces using generative adversarial networks"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "face parsing via recurrent propagation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-3 (induced by source paper "atypical facial landmark localisation with stacked hourglass networks a study on 3d facial modelling for medical diagnosis"):
  with members:
    - "3dpalsynet a facial palsy grading and motion recognition framework using fully 3d convolutional neural networks"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"
    - "stacked hourglass networks for human pose estimation"
    - "unconstrained facial landmark localization with backbone branches fully convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "facial landmark machines a backbone branches architecture with progressive representation learning"):
  with members:
    - "crowd counting using deep recurrent spatial aware network"
    - "quantized densely connected u nets for efficient landmark localization"
    - "contrast oriented deep neural networks for salient object detection"
    - "unsupervised discovery of object landmarks as structural representations"
    - "style aggregated network for facial landmark detection"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "attentive crowd flow machines"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"

Connected through hyperedge HE-5 (induced by source paper "combining data driven and model driven methods for robust facial landmark detection"):
  with members:
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "pose invariant face alignment with a single cnn"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "dense face alignment"
    - "simultaneous facial landmark detection pose and deformation estimation under facial occlusion"
    - "kepler keypoint and pose estimation of unconstrained faces by learning efficient h cnn regressors"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-6 (induced by source paper "facial landmark point localization using coarse to fine deep recurrent neural network"):
  with members:
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "improving landmark localization with semi supervised learning"
    - "adversarial learning of structure aware fully convolutional networks for landmark localization"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "recurrent human pose estimation"
    - "robust face alignment using a mixture of invariant experts"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "joint multi view face alignment in the wild"):
  with members:
    - "a comprehensive performance evaluation of deformable face tracking in the wild"
    - "densereg fully convolutional dense shape regression in the wild"
    - "arcface additive angular margin loss for deep face recognition"
    - "face detection using deep learning an improved faster rcnn approach"
    - "uv gan adversarial facial uv map completion for pose invariant face recognition"
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"
    - "robust facial landmark detection under significant head poses and occlusion"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-8 (induced by source paper "a recurrent encoder decoder network for sequential face alignment"):
  with members:
    - "face alignment in full pose range a 3d total solution"
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "weakly supervised disentangling with recurrent transformations for 3d view synthesis"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "deep cascaded regression for face alignment"
    - "conditional random fields as recurrent neural networks"
    - "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)