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: "a comprehensive performance evaluation of deformable face tracking in the wild"
  Abstract: Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as "in-the-wild"). This is partially attributed to the fact that comprehensive "in-the-wild" benchmarks have been developed for face detection, landmark localisation and recognition/verification. A very important technology that has not been thoroughly evaluated yet is deformable face tracking "in-the-wild". Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos. In this paper, we perform the first, to the best of our knowledge, thorough evaluation of state-of-the-art deformable face tracking pipelines using the recently introduced 300 VW benchmark. We evaluate many different architectures focusing mainly on the task of on-line deformable face tracking. In particular, we compare the following general strategies: (a) generic face detection plus generic facial landmark localisation, (b) generic model free tracking plus generic facial landmark localisation, as well as (c) hybrid approaches using state-of-the-art face detection, model free tracking and facial landmark localisation technologies. Our evaluation reveals future avenues for further research on the topic.

Connected through hyperedge HE-1 (induced by source paper "facial landmark detection a literature survey"):
  with members:
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
    - "facial feature tracking under varying facial expressions and face poses based on restricted boltzmann machines"
    - "robust facial landmark detection under significant head poses and occlusion"
    - "a hierarchical probabilistic model for facial feature detection"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "deepid3 face recognition with very deep neural networks"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-2 (induced by source paper "decafa deep convolutional cascade for face alignment in the wild"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "densereg fully convolutional dense shape regression in the wild"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "style aggregated network for facial landmark detection"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "deep alignment network a convolutional neural network for robust face alignment"

Connected through hyperedge HE-3 (induced by source paper "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"):
  with members:
    - "convolutional neural networks pretrained on large face recognition datasets for emotion classification from video"
    - "temporal multimodal fusion for video emotion classification in the wild"
    - "facial affect estimation in the wild using deep residual and convolutional networks"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "learning grimaces by watching tv"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "what is the relationship between face alignment and facial expression recognition"):
  with members:
    - "face alignment in full pose range a 3d total solution"
    - "advanced local motion patterns for macro and micro facial expression recognition"
    - "towards highly accurate and stable face alignment for high resolution videos"
    - "red net a recurrent encoder decoder network for video based face alignment"
    - "style aggregated network for facial landmark detection"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "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 "motion deblurring of faces"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "deep face deblurring"
    - "deep generative filter for motion deblurring"
    - "learning blind motion deblurring"
    - "motion deblurring in the wild"
    - "attention aware face hallucination via deep reinforcement learning"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "deblurgan blind motion deblurring using conditional adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "brute force facial landmark analysis with a 140 000 way classifier"):
  with members:
    - "a recurrent encoder decoder network for sequential face alignment"
    - "cascaded continuous regression for real time incremental face tracking"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "deep residual learning for image recognition"
    - "learning visual features from large weakly supervised data"
    - "the megaface benchmark 1 million faces for recognition at scale"
    - "wider face a face detection benchmark"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-7 (induced by source paper "deception detection by 2d to 3d face reconstruction from videos"):
  with members:
    - "unsupervised training for 3d morphable model regression"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "morphable face models an open framework"
    - "a compact formula for the derivative of a 3 d rotation in exponential coordinates"
    - "adam a method for stochastic optimization"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-8 (induced by source paper "joint multi view face alignment in the wild"):
  with members:
    - "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"
    - "face detection with end to end integration of a convnet and a 3d model"

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)