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: "unconstrained facial landmark localization with backbone branches fully convolutional networks"
  Abstract: This paper investigates how to rapidly and accurately localize facial landmarks in unconstrained, cluttered environments rather than in the well segmented face images. We present a novel Backbone-Branches Fully-Convolutional Neural Network (BB-FCN), which produces facial landmark response maps directly from raw images without relying on pre-process or sliding window approaches. BB-FCN contains one backbone and a number of network branches with each corresponding to one landmark type, and it operates in a progressive manner. Specifically, the backbone roughly detects the locations of facial landmarks by taking the whole image as input, and the branches further refine the localizations based on a local observation from the backbone's intermediate feature map. Moreover, our backbone-branches architecture does not contain full-connection layers for location regression, leading to efficient learning and inference. Our extensive experiments show that our model achieves superior performances over other state-of-the-arts under both the constrained (i.e. with face regions) and the "in the wild" scenarios.

Connected through hyperedge HE-1 (induced by source paper "3fabrec fast few shot face alignment by reconstruction"):
  with members:
    - "teacher supervises students how to learn from partially labeled images for facial landmark detection"
    - "few shot adversarial learning of realistic neural talking head models"
    - "aggregation via separation boosting facial landmark detector with semi supervised style translation"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "unsupervised learning of landmarks by descriptor vector exchange"
    - "adaptive wing loss for robust face alignment via heatmap regression"
    - "decafa deep convolutional cascade for face alignment in the wild"
    - "look at boundary a boundary aware face alignment algorithm"

Connected through hyperedge HE-2 (induced by source paper "face alignment in full pose range a 3d total solution"):
  with members:
    - "pose invariant face alignment with a single cnn"
    - "finding tiny faces"
    - "facial landmark detection with tweaked convolutional neural networks"
    - "human pose estimation with iterative error feedback"
    - "pose invariant 3d face alignment"
    - "effective face frontalization in unconstrained images"
    - "going deeper with convolutions"
    - "face expression recognition and analysis the state of the art"

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"
    - "deep recurrent regression for facial landmark detection"

Connected through hyperedge HE-4 (induced by source paper "joint 3d face reconstruction and dense alignment with position map regression network"):
  with members:
    - "multi attribute robust component analysis for facial uv maps"
    - "side information for face completion a robust pca approach"
    - "densereg fully convolutional dense shape regression in the wild"
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "end to end 3d face reconstruction with deep neural networks"
    - "learning dense facial correspondences in unconstrained images"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"

Connected through hyperedge HE-5 (induced by source paper "wing loss for robust facial landmark localisation with convolutional neural networks"):
  with members:
    - "evaluation of dense 3d reconstruction from 2d face images in the wild"
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "sphereface deep hypersphere embedding for face recognition"
    - "end to end 3d face reconstruction with deep neural networks"
    - "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"
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
    - "dense face alignment"

Connected through hyperedge HE-6 (induced by source paper "binge watching scaling affordance learning from sitcoms"):
  with members:
    - "learning a predictable and generative vector representation for objects"
    - "marr revisited 2d 3d alignment via surface normal prediction"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "the curious robot learning visual representations via physical interactions"
    - "single image 3d interpreter network"
    - "what happens if learning to predict the effect of forces in images"
    - "r fcn object detection via region based fully convolutional networks"

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 "interspecies knowledge transfer for facial keypoint detection"):
  with members:
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "do we really need to collect millions of faces for effective face recognition"
    - "what makes imagenet good for transfer learning"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "robust face alignment using a mixture of invariant experts"
    - "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)