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: "adversarial learning of structure aware fully convolutional networks for landmark localization"
  Abstract: Landmark/pose estimation in single monocular images have received much effort in computer vision due to its important applications. It remains a challenging task when input images severe occlusions caused by, e.g., adverse camera views. Under such circumstances, biologically implausible pose predictions may be produced. In contrast, human vision is able to predict poses by exploiting geometric constraints of landmark point inter-connectivity. To address the problem, by incorporating priors about the structure of pose components, we propose a novel structure-aware fully convolutional network to implicitly take such priors into account during training of the deep network. Explicit learning of such constraints is typically challenging. Instead, inspired by how human identifies implausible poses, we design discriminators to distinguish the real poses from the fake ones (such as biologically implausible ones). If the pose generator G generates results that the discriminator fails to distinguish from real ones, the network successfully learns the priors. Training of the network follows the strategy of conditional Generative Adversarial Networks (GANs). The effectiveness of the proposed network is evaluated on three pose-related tasks: 2D single human pose estimation, 2D facial landmark estimation and 3D single human pose estimation. The proposed approach significantly outperforms the state-of-the-art methods and almost always generates plausible pose predictions, demonstrating the usefulness of implicit learning of structures using GANs.

Connected through hyperedge HE-1 (induced by source paper "multi scale aggregation r cnn for 2d multi person pose estimation"):
  with members:
    - "posefix model agnostic general human pose refinement network"
    - "simple baselines for human pose estimation and tracking"
    - "multiposenet fast multi person pose estimation using pose residual network"
    - "benchmarking and error diagnosis in multi instance pose estimation"
    - "megdet a large mini batch object detector"
    - "towards accurate multi person pose estimation in the wild"
    - "cascaded pyramid network for multi person pose estimation"
    - "multi context attention for human pose estimation"

Connected through hyperedge HE-2 (induced by source paper "deep robust and single shot 3d multi person human pose estimation in complex images"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "3d human pose estimation in the wild by adversarial learning"
    - "learning to detect and track visible and occluded body joints in a virtual world"
    - "learning pose grammar to encode human body configuration for 3d pose estimation"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "towards accurate multi person pose estimation in the wild"
    - "compositional human pose regression"
    - "a simple yet effective baseline for 3d human pose estimation"

Connected through hyperedge HE-3 (induced by source paper "3d human pose estimation in the wild by adversarial learning"):
  with members:
    - "learning to generate long term future via hierarchical prediction"
    - "cycada cycle consistent adversarial domain adaptation"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "progressive growing of gans for improved quality stability and variation"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "learning feature pyramids for human pose estimation"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"

Connected through hyperedge HE-4 (induced by source paper "high diversity attribute guided face generation with gans"):
  with members:
    - "mcgan mean and covariance feature matching gan"
    - "began boundary equilibrium generative adversarial networks"
    - "it takes only two adversarial generator encoder networks"
    - "recent progress of face image synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "focal loss for dense object detection"
    - "controllable generative adversarial network"
    - "face aging with conditional generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "discovery of latent 3d keypoints via end to end geometric reasoning"):
  with members:
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "unsupervised discovery of object landmarks as structural representations"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "densepose dense human pose estimation in the wild"
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "learning feature pyramids for human pose estimation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-6 (induced by source paper "facial landmark point localization using coarse to fine deep recurrent neural network"):
  with members:
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "improving landmark localization with semi supervised learning"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "recurrent human pose estimation"
    - "robust face alignment using a mixture of invariant experts"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "two stage convolutional part heatmap regression for the 1st 3d face alignment in the wild 3dfaw challenge"
    - "face alignment using k cluster regression forests with weighted splitting"

Connected through hyperedge HE-7 (induced by source paper "towards highly accurate and stable face alignment for high resolution videos"):
  with members:
    - "facial landmarks detection by self iterative regression based landmarks attention network"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "multi context attention for human pose estimation"
    - "stacked hourglass networks for human pose estimation"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "learning deep representation for face alignment with auxiliary attributes"

Connected through hyperedge HE-8 (induced by source paper "fsrnet end to end learning face super resolution with facial priors"):
  with members:
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "generative face completion"
    - "pose invariant face alignment with a single cnn"
    - "attention aware face hallucination via deep reinforcement learning"
    - "scale aware face detection"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "memnet a persistent memory network for image restoration"
    - "learning to hallucinate face images via component generation and enhancement"

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)