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: "red net a recurrent encoder decoder network for video based face alignment"
  Abstract: We propose a novel method for real-time face alignment in videos based on a recurrent encoder-decoder network model. Our proposed model predicts 2D facial point heat maps regularized by both detection and regression loss, while uniquely exploiting recurrent learning at both spatial and temporal dimensions. At the spatial level, we add a feedback loop connection between the combined output response map and the input, in order to enable iterative coarse-to-fine face alignment using a single network model, instead of relying on traditional cascaded model ensembles. At the temporal level, we first decouple the features in the bottleneck of the network into temporal-variant factors, such as pose and expression, and temporal-invariant factors, such as identity information. Temporal recurrent learning is then applied to the decoupled temporal-variant features. We show that such feature disentangling yields better generalization and significantly more accurate results at test time. We perform a comprehensive experimental analysis, showing the importance of each component of our proposed model, as well as superior results over the state of the art and several variations of our method in standard datasets.

Connected through hyperedge HE-1 (induced by source paper "laplace landmark localization"):
  with members:
    - "ev action electromyography vision multi modal action dataset"
    - "3d guided fine grained face manipulation"
    - "video to video synthesis"
    - "densereg fully convolutional dense shape regression in the wild"
    - "3d human pose estimation in the wild by adversarial learning"
    - "animating arbitrary objects via deep motion transfer"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "polarimetric thermal to visible face verification via attribute preserved synthesis"):
  with members:
    - "thermal to visible synthesis of face images using multiple regions"
    - "face synthesis from visual attributes via sketch using conditional vaes and gans"
    - "tv gan generative adversarial network based thermal to visible face recognition"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-3 (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"
    - "a comprehensive performance evaluation of deformable face tracking in the wild"
    - "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-4 (induced by source paper "quantized densely connected u nets for efficient landmark localization"):
  with members:
    - "learning to forecast and refine residual motion for image to video generation"
    - "training and inference with integers in deep neural networks"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "deeprebirth accelerating deep neural network execution on mobile devices"
    - "learning feature pyramids for human pose estimation"
    - "memory efficient implementation of densenets"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"

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)