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: "cascaded continuous regression for real time incremental face tracking"
  Abstract: This paper introduces a novel real-time algorithm for facial landmark tracking. Compared to detection, tracking has both additional challenges and opportunities. Arguably the most important aspect in this domain is updating a tracker's models as tracking progresses, also known as incremental (face) tracking. While this should result in more accurate localisation, how to do this online and in real time without causing a tracker to drift is still an important open research question. We address this question in the cascaded regression framework, the state-of-the-art approach for facial landmark localisation. Because incremental learning for cascaded regression is costly, we propose a much more efficient yet equally accurate alternative using continuous regression. More specifically, we first propose cascaded continuous regression (CCR) and show its accuracy is equivalent to the Supervised Descent Method. We then derive the incremental learning updates for CCR (iCCR) and show that it is an order of magnitude faster than standard incremental learning for cascaded regression, bringing the time required for the update from seconds down to a fraction of a second, thus enabling real-time tracking. Finally, we evaluate iCCR and show the importance of incremental learning in achieving state-of-the-art performance. Code for our iCCR is available from this http URL

Connected through hyperedge HE-1 (induced by source paper "towards omni supervised face alignment for large scale unlabeled videos"):
  with members:
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "look at boundary a boundary aware face alignment algorithm"
    - "towards highly accurate and stable face alignment for high resolution videos"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "relation networks for object detection"
    - "pose invariant face alignment with a single cnn"
    - "self adversarial training for human pose estimation"

Connected through hyperedge HE-2 (induced by source paper "brute force facial landmark analysis with a 140 000 way classifier"):
  with members:
    - "a comprehensive performance evaluation of deformable face tracking in the wild"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "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-3 (induced by source paper "joint action unit localisation and intensity estimation through heatmap regression"):
  with members:
    - "a functional regression approach to facial landmark tracking"
    - "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"
    - "stacked hourglass networks for human pose estimation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "a functional regression approach to facial landmark tracking"):
  with members:
    - "a novel performance evaluation methodology for single target trackers"
    - "supervised descent method for solving nonlinear least squares problems in computer vision"

Connected through hyperedge HE-5 (induced by source paper "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"):
  with members:
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "identity mappings in deep residual networks"
    - "deepercut a deeper stronger and faster multi person pose estimation model"
    - "two stage convolutional part heatmap regression for the 1st 3d face alignment in the wild 3dfaw challenge"
    - "human pose estimation via convolutional part heatmap regression"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "fera 2017 addressing head pose in the third facial expression recognition and analysis challenge"):
  with members:
    - "a functional regression approach to facial landmark tracking"

Connected through hyperedge HE-7 (induced by source paper "a cnn cascade for landmark guided semantic part segmentation"):
  with members:
    - "fine grained pose prediction normalization and recognition"
    - "conditional random fields as recurrent neural networks"
    - "deep learning for semantic part segmentation with high level guidance"
    - "human pose estimation with iterative error feedback"
    - "learning deconvolution network for semantic segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "caffe convolutional architecture for fast feature embedding"

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"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "facial feature point detection a comprehensive survey"

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)