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: "face alignment assisted by head pose estimation"
  Abstract: In this paper we propose a supervised initialization scheme for cascaded face alignment based on explicit head pose estimation. We first investigate the failure cases of most state of the art face alignment approaches and observe that these failures often share one common global property, i.e. the head pose variation is usually large. Inspired by this, we propose a deep convolutional network model for reliable and accurate head pose estimation. Instead of using a mean face shape, or randomly selected shapes for cascaded face alignment initialisation, we propose two schemes for generating initialisation: the first one relies on projecting a mean 3D face shape (represented by 3D facial landmarks) onto 2D image under the estimated head pose; the second one searches nearest neighbour shapes from the training set according to head pose distance. By doing so, the initialisation gets closer to the actual shape, which enhances the possibility of convergence and in turn improves the face alignment performance. We demonstrate the proposed method on the benchmark 300W dataset and show very competitive performance in both head pose estimation and face alignment.

Connected through hyperedge HE-1 (induced by source paper "pfld a practical facial landmark detector"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "exploring disentangled feature representation beyond face identification"
    - "style aggregated network for facial landmark detection"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "improving landmark localization with semi supervised learning"

Connected through hyperedge HE-2 (induced by source paper "facial pose estimation by deep learning from label distributions"):
  with members:
    - "a realistic face to face conversation system based on deep neural networks"
    - "video synthesis of human upper body with realistic face"
    - "a neural virtual anchor synthesizer based on seq2seq and gan models"
    - "detecting gaze towards eyes in natural social interactions and its use in child assessment"
    - "pose robust face recognition via deep residual equivariant mapping"
    - "fine grained head pose estimation without keypoints"
    - "deep label distribution learning with label ambiguity"
    - "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-3 (induced by source paper "a driver fatigue recognition algorithm based on spatio temporal feature sequence"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "unitbox an advanced object detection network"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "compact convolutional neural network cascade for face detection"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "a vision based system for monitoring the loss of attention in automotive drivers"
    - "network in network"

Connected through hyperedge HE-4 (induced by source paper "nose eyes and ears head pose estimation by locating facial keypoints"):
  with members:
    - "robust head pose estimation based on partially latent mixture of linear regressions"
    - "kepler keypoint and pose estimation of unconstrained faces by learning efficient h cnn regressors"
    - "single image 3d interpreter network"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "sparseness meets deepness 3d human pose estimation from monocular video"

Connected through hyperedge HE-5 (induced by source paper "interspecies knowledge transfer for facial keypoint detection"):
  with members:
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "do we really need to collect millions of faces for effective face recognition"
    - "what makes imagenet good for transfer learning"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "robust face alignment using a mixture of invariant experts"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "fine grained head pose estimation without keypoints"):
  with members:
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "faceposenet making a case for landmark free face alignment"
    - "dockerface an easy to install and use faster r cnn face detector in a docker container"
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"
    - "kepler keypoint and pose estimation of unconstrained faces by learning efficient h cnn regressors"
    - "stacked hourglass networks for human pose estimation"
    - "face detection with the faster r cnn"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"

Connected through hyperedge HE-7 (induced by source paper "dense face alignment"):
  with members:
    - "3d face morphable models in the wild"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "multi directional multi level dual cross patterns for robust face recognition"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "holistically nested edge detection"
    - "pose invariant 3d face alignment"
    - "mirror mirror on the wall tell me is the error small"
    - "transferring landmark annotations for cross dataset face alignment"

Connected through hyperedge HE-8 (induced by source paper "deep deformation network for object landmark localization"):
  with members:
    - "warpnet weakly supervised matching for single view reconstruction"
    - "spatial transformer networks"
    - "learning to see by moving"
    - "combining local appearance and holistic view dual source deep neural networks for human pose estimation"
    - "deepbox learning objectness with convolutional networks"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "very deep convolutional networks for large scale image recognition"
    - "deep learning face attributes in the wild"

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)