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: "tabletgaze unconstrained appearance based gaze estimation in mobile tablets"
  Abstract: We study gaze estimation on tablets, our key design goal is uncalibrated gaze estimation using the front-facing camera during natural use of tablets, where the posture and method of holding the tablet is not constrained. We collected the first large unconstrained gaze dataset of tablet users, labeled Rice TabletGaze dataset. The dataset consists of 51 subjects, each with 4 different postures and 35 gaze locations. Subjects vary in race, gender and in their need for prescription glasses, all of which might impact gaze estimation accuracy. Driven by our observations on the collected data, we present a TabletGaze algorithm for automatic gaze estimation using multi-level HoG feature and Random Forests regressor. The TabletGaze algorithm achieves a mean error of 3.17 cm. We perform extensive evaluation on the impact of various factors such as dataset size, race, wearing glasses and user posture on the gaze estimation accuracy and make important observations about the impact of these factors.

Connected through hyperedge HE-1 (induced by source paper "unsupervised representation learning for gaze estimation"):
  with members:
    - "gaze contingent ocular parallax rendering for virtual reality"
    - "appearance based gaze estimation via gaze decomposition and single gaze point calibration"
    - "openeds open eye dataset"
    - "a differential approach for gaze estimation"
    - "connecting gaze scene and attention generalized attention estimation via joint modeling of gaze and scene saliency"
    - "self supervised learning of a facial attribute embedding from video"
    - "learning to personalize in appearance based gaze tracking"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"

Connected through hyperedge HE-2 (induced by source paper "a differential approach for gaze estimation"):
  with members:
    - "improving few shot user specific gaze adaptation via gaze redirection synthesis"
    - "efficient eye typing with 9 direction gaze estimation"
    - "from depth data to head pose estimation a siamese approach"
    - "learning to compare relation network for few shot learning"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "3d gaze estimation from 2d pupil positions on monocular head mounted eye trackers"
    - "eye tracking for everyone"

Connected through hyperedge HE-3 (induced by source paper "improving few shot user specific gaze adaptation via gaze redirection synthesis"):
  with members:
    - "recurrent cnn for 3d gaze estimation using appearance and shape cues"
    - "deep pictorial gaze estimation"
    - "appearance based 3d gaze estimation with personal calibration"
    - "ganimation anatomically aware facial animation from a single image"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "gazedirector fully articulated eye gaze redirection in video"
    - "eye tracking for everyone"

Connected through hyperedge HE-4 (induced by source paper "unsupervised learning of eye gaze representation from the web"):
  with members:
    - "unsupervised learning of face representations"
    - "prediction and localization of student engagement in the wild"
    - "speech gesture mapping and engagement evaluation in human robot interaction"
    - "unsupervised learning of depth and ego motion from video"
    - "dynamic routing between capsules"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "eye tracking for everyone"
    - "shuffle and learn unsupervised learning using temporal order verification"

Connected through hyperedge HE-5 (induced by source paper "evaluation of appearance based methods and implications for gaze based applications"):
  with members:
    - "forecasting user attention during everyday mobile interactions using device integrated and wearable sensors"
    - "detecting low rapport during natural interactions in small groups from non verbal behaviour"
    - "learning to find eye region landmarks for remote gaze estimation in unconstrained settings"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "learning from simulated and unsupervised images through adversarial training"
    - "it s written all over your face full face appearance based gaze estimation"
    - "rendering of eyes for eye shape registration and gaze estimation"
    - "prediction of search targets from fixations in open world settings"

Connected through hyperedge HE-6 (induced by source paper "forecasting user attention during everyday mobile interactions using device integrated and wearable sensors"):
  with members:
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "going deeper into first person activity recognition"
    - "conditional random fields as recurrent neural networks"
    - "appearance based gaze estimation in the wild"
    - "the evolution of first person vision methods a survey"
    - "places205 vggnet models for scene recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-7 (induced by source paper "light weight head pose invariant gaze tracking"):
  with members:
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "eye tracking for everyone"
    - "learning from simulated and unsupervised images through adversarial training"
    - "it s written all over your face full face appearance based gaze estimation"
    - "deep residual learning for image recognition"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "rendering of eyes for eye shape registration and gaze estimation"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "bubbleview an interface for crowdsourcing image importance maps and tracking visual attention"):
  with members:
    - "learning visual importance for graphic designs and data visualizations"
    - "saliency revisited analysis of mouse movements versus fixations"
    - "eye tracking for everyone"
    - "shallow and deep convolutional networks for saliency prediction"
    - "what do different evaluation metrics tell us about saliency models"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "cat2000 a large scale fixation dataset for boosting saliency research"
    - "appearance based gaze estimation 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)