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: "self supervised learning of a facial attribute embedding from video"
  Abstract: We propose a self-supervised framework for learning facial attributes by simply watching videos of a human face speaking, laughing, and moving over time. To perform this task, we introduce a network, Facial Attributes-Net (FAb-Net), that is trained to embed multiple frames from the same video face-track into a common low-dimensional space. With this approach, we make three contributions: first, we show that the network can leverage information from multiple source frames by predicting confidence/attention masks for each frame; second, we demonstrate that using a curriculum learning regime improves the learned embedding; finally, we demonstrate that the network learns a meaningful face embedding that encodes information about head pose, facial landmarks and facial expression, i.e. facial attributes, without having been supervised with any labelled data. We are comparable or superior to state-of-the-art self-supervised methods on these tasks and approach the performance of supervised methods.

Connected through hyperedge HE-1 (induced by source paper "mast a memory augmented self supervised tracker"):
  with members:
    - "video object segmentation using space time memory networks"
    - "self supervised learning for video correspondence flow"
    - "multigrid predictive filter flow for unsupervised learning on videos"
    - "learning correspondence from the cycle consistency of time"
    - "joint task self supervised learning for temporal correspondence"
    - "unsupervised deep tracking"
    - "rvos end to end recurrent network for video object segmentation"
    - "feelvos fast end to end embedding learning for video object segmentation"

Connected through hyperedge HE-2 (induced by source paper "low budget unsupervised label query through domain alignment enforcement"):
  with members:
    - "self supervised learning with geometric constraints in monocular video connecting flow depth and camera"
    - "image difficulty curriculum for generative adversarial networks cugan"
    - "unsupervised domain adaptation via regularized conditional alignment"
    - "confidence regularized self training"
    - "beyond sharing weights for deep domain adaptation"
    - "domain generalization by solving jigsaw puzzles"
    - "consistency based semi supervised active learning towards minimizing labeling budget"
    - "self paced deep learning for weakly supervised object detection"

Connected through hyperedge HE-3 (induced by source paper "semi supervised unconstrained action unit detection via latent feature domain"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "t2net synthetic to realistic translation for solving single image depth estimation tasks"
    - "facial action unit detection using attention and relation learning"
    - "residual parameter transfer for deep domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "hand keypoint detection in single images using multiview bootstrapping"

Connected through hyperedge HE-4 (induced by source paper "deformation flow based two stream network for lip reading"):
  with members:
    - "learning spatio temporal features with two stream deep 3d cnns for lipreading"
    - "multi grained spatio temporal modeling for lip reading"
    - "lrw 1000 a naturally distributed large scale benchmark for lip reading in the wild"
    - "on the integration of optical flow and action recognition"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "lipnet end to end sentence level lipreading"
    - "combining residual networks with lstms for lipreading"
    - "lip reading sentences in the wild"

Connected through hyperedge HE-5 (induced by source paper "learning landmarks from unaligned data using image translation"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "learning to read by spelling towards unsupervised text recognition"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "3d human pose estimation in the wild by adversarial learning"
    - "unsupervised discovery of object landmarks as structural representations"
    - "it s all relative monocular 3d human pose estimation from weakly supervised data"

Connected through hyperedge HE-6 (induced by source paper "learning temporal pose estimation from sparsely labeled videos"):
  with members:
    - "learning correspondence from the cycle consistency of time"
    - "deep high resolution representation learning for human pose estimation"
    - "multi domain pose network for multi person pose estimation and tracking"
    - "pose flow efficient online pose tracking"
    - "simple baselines for human pose estimation and tracking"
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "focal loss for dense object detection"
    - "deformable convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "revisiting self supervised visual representation learning"):
  with members:
    - "grasp2vec learning object representations from self supervised grasping"
    - "learning image representations by completing damaged jigsaw puzzles"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "cross and learn cross modal self supervision"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"
    - "invertible residual networks"

Connected through hyperedge HE-8 (induced by source paper "point to point video generation"):
  with members:
    - "diversity sensitive conditional generative adversarial networks"
    - "phasenet for video frame interpolation"
    - "conditional video generation using action appearance captions"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "futuregan anticipating the future frames of video sequences using spatio temporal 3d convolutions in progressively growing gans"
    - "stochastic adversarial video prediction"
    - "probabilistic video generation using holistic attribute control"
    - "stochastic video generation with a learned prior"

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)