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: "an occluded stacked hourglass approach to facial landmark localization and occlusion estimation"
  Abstract: A key step to driver safety is to observe the driver's activities with the face being a key step in this process to extracting information such as head pose, blink rate, yawns, talking to passenger which can then help derive higher level information such as distraction, drowsiness, intent, and where they are looking. In the context of driving safety, it is important for the system perform robust estimation under harsh lighting and occlusion but also be able to detect when the occlusion occurs so that information predicted from occluded parts of the face can be taken into account properly. This paper introduces the Occluded Stacked Hourglass, based on the work of original Stacked Hourglass network for body pose joint estimation, which is retrained to process a detected face window and output 68 occlusion heat maps, each corresponding to a facial landmark. Landmark location, occlusion levels and a refined face detection score, to reject false positives, are extracted from these heat maps. Using the facial landmark locations, features such as head pose and eye/mouth openness can be extracted to derive driver attention and activity. The system is evaluated for face detection, head pose, and occlusion estimation on various datasets in the wild, both quantitatively and qualitatively, and shows state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "spacenet mvoi a multi view overhead imagery dataset"):
  with members:
    - "spacenet a remote sensing dataset and challenge series"
    - "xview objects in context in overhead imagery"
    - "spatially transformed adversarial examples"
    - "the effects of super resolution on object detection performance in satellite imagery"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "you only look twice rapid multi scale object detection in satellite imagery"
    - "why do deep convolutional networks generalize so poorly to small image transformations"

Connected through hyperedge HE-2 (induced by source paper "lightweight real time makeup try on in mobile browsers with tiny cnn models for facial tracking"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "mask r cnn"
    - "stacked hourglass networks for human pose estimation"
    - "human pose estimation via convolutional part heatmap regression"

Connected through hyperedge HE-3 (induced by source paper "synthetic occlusion augmentation with volumetric heatmaps for the 2018 eccv posetrack challenge on 3d human pose estimation"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "multi scale structure aware network for human pose estimation"
    - "yolov3 an incremental improvement"
    - "modeling visual context is key to augmenting object detection datasets"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "how robust is 3d human pose estimation to occlusion"
    - "3d human pose estimation with 2d marginal heatmaps"
    - "numerical coordinate regression with convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "how robust is 3d human pose estimation to occlusion"):
  with members:
    - "multi scale structure aware network for human pose estimation"
    - "modeling visual context is key to augmenting object detection datasets"
    - "3d human pose estimation with 2d marginal heatmaps"
    - "numerical coordinate regression with convolutional neural networks"
    - "random erasing data augmentation"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "compositional human pose regression"
    - "integral human pose regression"

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)