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: "fine grained head pose estimation without keypoints"
  Abstract: Estimating the head pose of a person is a crucial problem that has a large amount of applications such as aiding in gaze estimation, modeling attention, fitting 3D models to video and performing face alignment. Traditionally head pose is computed by estimating some keypoints from the target face and solving the 2D to 3D correspondence problem with a mean human head model. We argue that this is a fragile method because it relies entirely on landmark detection performance, the extraneous head model and an ad-hoc fitting step. We present an elegant and robust way to determine pose by training a multi-loss convolutional neural network on 300W-LP, a large synthetically expanded dataset, to predict intrinsic Euler angles (yaw, pitch and roll) directly from image intensities through joint binned pose classification and regression. We present empirical tests on common in-the-wild pose benchmark datasets which show state-of-the-art results. Additionally we test our method on a dataset usually used for pose estimation using depth and start to close the gap with state-of-the-art depth pose methods. We open-source our training and testing code as well as release our pre-trained models.

Connected through hyperedge HE-1 (induced by source paper "touchless typing using head movement based gestures"):
  with members:
    - "hierarchical deep feature learning for decoding imagined speech from eeg"
    - "efficient eye typing with 9 direction gaze estimation"
    - "sequence to sequence learning as beam search optimization"

Connected through hyperedge HE-2 (induced by source paper "learning deep conditional target densities for accurate regression"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "deep high resolution representation learning for human pose estimation"
    - "bottom up object detection by grouping extreme and center points"
    - "lightweight probabilistic deep networks"
    - "cornernet detecting objects as paired keypoints"
    - "atom accurate tracking by overlap maximization"
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"
    - "bounding box regression with uncertainty for accurate object detection"

Connected through hyperedge HE-3 (induced by source paper "maskgan towards diverse and interactive facial image manipulation"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "instance level facial attributes transfer with geometry aware flow"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"

Connected through hyperedge HE-4 (induced by source paper "tree gated deep mixture of experts for pose robust face alignment"):
  with members:
    - "face alignment in full pose range a 3d total solution"
    - "tree gated deep regressor ensemble for face alignment in the wild"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "adaptive neural trees"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "pose invariant face alignment with a single cnn"
    - "improving landmark localization with semi supervised learning"
    - "deep forest towards an alternative to deep neural networks"

Connected through hyperedge HE-5 (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"
    - "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"
    - "kepler keypoint and pose estimation of unconstrained faces by learning efficient h cnn regressors"

Connected through hyperedge HE-6 (induced by source paper "graph representation for face analysis in image collections"):
  with members:
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "arcface additive angular margin loss for deep face recognition"
    - "a style based generator architecture for generative adversarial networks"
    - "face clustering representation and pairwise constraints"
    - "real time convolutional neural networks for emotion and gender classification"
    - "face detection using deep learning an improved faster rcnn approach"
    - "clustering millions of faces by identity"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "laeo net revisiting people looking at each other in videos"):
  with members:
    - "connecting gaze scene and attention generalized attention estimation via joint modeling of gaze and scene saliency"
    - "learnable pins cross modal embeddings for person identity"
    - "tracking gaze and visual focus of attention of people involved in social interaction"
    - "robust head pose estimation based on partially latent mixture of linear regressions"
    - "ridiculously fast shot boundary detection with fully convolutional neural networks"
    - "ssd single shot multibox detector"
    - "online real time multiple spatiotemporal action localisation and prediction"
    - "anticipating visual representations from unlabeled video"

Connected through hyperedge HE-8 (induced by source paper "hybrid coarse fine classification for head pose estimation"):
  with members:
    - "nose eyes and ears head pose estimation by locating facial keypoints"
    - "robust head pose estimation based on partially latent mixture of linear regressions"
    - "faceposenet making a case for landmark free face alignment"
    - "kepler keypoint and pose estimation of unconstrained faces by learning efficient h cnn regressors"
    - "an overview of multi task learning in deep neural networks"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "an all in one convolutional neural network for face analysis"
    - "adam a method for stochastic optimization"

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)