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: "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
  Abstract: This paper investigates how far a very deep neural network is from attaining close to saturating performance on existing 2D and 3D face alignment datasets. To this end, we make the following 5 contributions: (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and finally evaluate it on all other 2D facial landmark datasets. (b)We create a guided by 2D landmarks network which converts 2D landmark annotations to 3D and unifies all existing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images). (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W. (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network. (e) We show that both 2D and 3D face alignment networks achieve performance of remarkable accuracy which is probably close to saturating the datasets used. Training and testing code as well as the dataset can be downloaded from https://www.adrianbulat.com/face-alignment/

Connected through hyperedge HE-1 (induced by source paper "first order motion model for image animation"):
  with members:
    - "textured neural avatars"
    - "few shot adversarial learning of realistic neural talking head models"
    - "dwnet dense warp based network for pose guided human video generation"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "gesture to gesture translation in the wild via category independent conditional maps"
    - "3d guided fine grained face manipulation"
    - "video to video synthesis"
    - "learning to forecast and refine residual motion for image to video generation"

Connected through hyperedge HE-2 (induced by source paper "hierarchical binary cnns for landmark localization with limited resources"):
  with members:
    - "godp globally optimized dual pathway deep network architecture for facial landmark localization in the wild"
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "aggregated residual transformations for deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-3 (induced by source paper "task assisted domain adaptation with anchor tasks"):
  with members:
    - "domain adaptation for structured output via discriminative patch representations"
    - "learning to adapt structured output space for semantic segmentation"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "taskonomy disentangling task transfer learning"
    - "3d human pose estimation in the wild by adversarial learning"
    - "regularizing deep networks by modeling and predicting label structure"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"

Connected through hyperedge HE-4 (induced by source paper "face hallucination with finishing touches"):
  with members:
    - "representation learning by rotating your faces"
    - "multi prototype networks for unconstrained set based face recognition"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "learning to see in the dark"
    - "non local neural networks"
    - "sphereface deep hypersphere embedding for face recognition"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "fsrnet end to end learning face super resolution with facial priors"

Connected through hyperedge HE-5 (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"
    - "simultaneous facial landmark detection pose and deformation estimation under facial occlusion"
    - "more is less a more complicated network with less inference complexity"
    - "mask r cnn"

Connected through hyperedge HE-6 (induced by source paper "pixel in pixel net towards efficient facial landmark detection in the wild"):
  with members:
    - "how much position information do convolutional neural networks encode"
    - "semantic alignment finding semantically consistent ground truth for facial landmark detection"
    - "teacher supervises students how to learn from partially labeled images for facial landmark detection"
    - "aggregation via separation boosting facial landmark detector with semi supervised style translation"
    - "high resolution representations for labeling pixels and regions"
    - "look at boundary a boundary aware face alignment algorithm"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "simple baselines for human pose estimation and tracking"

Connected through hyperedge HE-7 (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"
    - "joint multi view face alignment in the wild"

Connected through hyperedge HE-8 (induced by source paper "learning to validate the quality of detected landmarks"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "style aggregated network for facial landmark detection"
    - "sphereface deep hypersphere embedding for face recognition"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "neural aggregation network for video face recognition"

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)