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: "learning deep representation for face alignment with auxiliary attributes"
  Abstract: In this study, we show that landmark detection or face alignment task is not a single and independent problem. Instead, its robustness can be greatly improved with auxiliary information. Specifically, we jointly optimize landmark detection together with the recognition of heterogeneous but subtly correlated facial attributes, such as gender, expression, and appearance attributes. This is non-trivial since different attribute inference tasks have different learning difficulties and convergence rates. To address this problem, we formulate a novel tasks-constrained deep model, which not only learns the inter-task correlation but also employs dynamic task coefficients to facilitate the optimization convergence when learning multiple complex tasks. Extensive evaluations show that the proposed task-constrained learning (i) outperforms existing face alignment methods, especially in dealing with faces with severe occlusion and pose variation, and (ii) reduces model complexity drastically compared to the state-of-the-art methods based on cascaded deep model.

Connected through hyperedge HE-1 (induced by source paper "robust facial landmark detection via aggregation on geometrically manipulated faces"):
  with members:
    - "spatially transformed adversarial examples"
    - "fast geometrically perturbed adversarial faces"
    - "style aggregated network for facial landmark detection"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "pose invariant face alignment with a single cnn"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "simultaneous facial landmark detection pose and deformation estimation under facial occlusion"
    - "more is less a more complicated network with less inference complexity"

Connected through hyperedge HE-2 (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"
    - "self supervised learning of a facial attribute embedding from video"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "cross modal multi task learning for graphic recognition of caricature face"):
  with members:
    - "multi task convolutional neural network for pose invariant face recognition"
    - "deep cross modal learning for caricature verification and identification cavinet"
    - "sphereface deep hypersphere embedding for face recognition"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "an overview of multi task learning in deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "jaa net joint facial action unit detection and face alignment via adaptive attention"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "deep structure inference network for facial action unit recognition"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "facial action unit detection using attention and relation learning"
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
    - "fera 2017 addressing head pose in the third facial expression recognition and analysis challenge"
    - "simultaneous facial landmark detection pose and deformation estimation under facial occlusion"
    - "hand keypoint detection in single images using multiview bootstrapping"

Connected through hyperedge HE-5 (induced by source paper "multistage model for robust face alignment using deep neural networks"):
  with members:
    - "joint face alignment and 3d face reconstruction with application to face recognition"
    - "face alignment using a 3d deeply initialized ensemble of regression trees"
    - "look at boundary a boundary aware face alignment algorithm"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "style aggregated network for facial landmark detection"
    - "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-6 (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"
    - "self supervised learning of a facial attribute embedding from video"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "single network whole body pose estimation"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "recycle gan unsupervised video retargeting"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "multi scale structure aware network for human pose estimation"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "simple baselines for human pose estimation and tracking"
    - "monocular total capture posing face body and hands in the wild"
    - "efficient online multi person 2d pose tracking with recurrent spatio temporal affinity fields"

Connected through hyperedge HE-8 (induced by source paper "aggregation via separation boosting facial landmark detector with semi supervised style translation"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "synthesizing images of humans in unseen poses"
    - "unsupervised person image synthesis in arbitrary poses"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "taskonomy disentangling task transfer learning"
    - "multimodal unsupervised image to image translation"
    - "style aggregated network for facial landmark detection"

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)