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: "robust head pose estimation based on partially latent mixture of linear regressions"
  Abstract: Head-pose estimation has many applications, such as social event analysis, human-robot and human-computer interaction, driving assistance, and so forth. Head-pose estimation is challenging, because it must cope with changing illumination conditions, variabilities in face orientation and in appearance, partial occlusions of facial landmarks, as well as bounding-box-to-face alignment errors. We propose to use a mixture of linear regressions with partially-latent output. This regression method learns to map high-dimensional feature vectors (extracted from bounding boxes of faces) onto the joint space of head-pose angles and bounding-box shifts, such that they are robustly predicted in the presence of unobservable phenomena. We describe in detail the mapping method that combines the merits of unsupervised manifold learning techniques and of mixtures of regressions. We validate our method with three publicly available data sets and we thoroughly benchmark four variants of the proposed algorithm with several state-of-the-art head-pose estimation methods.

Connected through hyperedge HE-1 (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"
    - "fine grained head pose estimation without keypoints"
    - "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-2 (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"
    - "fine grained head pose estimation without keypoints"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "single image based head pose estimation with spherical parameterization and 3d morphing"):
  with members:
    - "searching for mobilenetv3"
    - "fine grained head pose estimation without keypoints"
    - "from depth data to head pose estimation a siamese approach"
    - "owl and lizard patterns of head pose and eye pose in driver gaze classification"

Connected through hyperedge HE-4 (induced by source paper "a comprehensive analysis of deep regression"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "lstm a search space odyssey"
    - "learning feature pyramids for human pose estimation"
    - "viraliency pooling local virality"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "deep convolutional neural network design patterns"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "on architectural choices in deep learning from network structure to gradient convergence and parameter estimation"

Connected through hyperedge HE-5 (induced by source paper "deepgum learning deep robust regression with a gaussian uniform mixture model"):
  with members:
    - "a comprehensive analysis of deep regression"
    - "viraliency pooling local virality"
    - "how to make an image more memorable a deep style transfer approach"
    - "learning from noisy labels with distillation"
    - "em algorithms for weighted data clustering with application to audio visual scene analysis"
    - "fashion landmark detection in the wild"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "robust optimization for deep regression"

Connected through hyperedge HE-6 (induced by source paper "nose eyes and ears head pose estimation by locating facial keypoints"):
  with members:
    - "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"
    - "face alignment assisted by head pose estimation"

Connected through hyperedge HE-7 (induced by source paper "tracking gaze and visual focus of attention of people involved in social interaction"):
  with members:
    - "audio visual speaker diarization based on spatiotemporal bayesian fusion"
    - "multiple speaker localization based on direct path features and likelihood maximization with spatial sparsity regularization"
    - "eye tracking for everyone"

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)