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: "analysis of hand segmentation in the wild"
  Abstract: A large number of works in egocentric vision have concentrated on action and object recognition. Detection and segmentation of hands in first-person videos, however, has less been explored. For many applications in this domain, it is necessary to accurately segment not only hands of the camera wearer but also the hands of others with whom he is interacting. Here, we take an in-depth look at the hand segmentation problem. In the quest for robust hand segmentation methods, we evaluated the performance of the state of the art semantic segmentation methods, off the shelf and fine-tuned, on existing datasets. We fine-tune RefineNet, a leading semantic segmentation method, for hand segmentation and find that it does much better than the best contenders. Existing hand segmentation datasets are collected in the laboratory settings. To overcome this limitation, we contribute by collecting two new datasets: a) EgoYouTubeHands including egocentric videos containing hands in the wild, and b) HandOverFace to analyze the performance of our models in presence of similar appearance occlusions. We further explore whether conditional random fields can help refine generated hand segmentations. To demonstrate the benefit of accurate hand maps, we train a CNN for hand-based activity recognition and achieve higher accuracy when a CNN was trained using hand maps produced by the fine-tuned RefineNet. Finally, we annotate a subset of the EgoHands dataset for fine-grained action recognition and show that an accuracy of 58.6% can be achieved by just looking at a single hand pose which is much better than the chance level (12.5%).

Connected through hyperedge HE-1 (induced by source paper "analysis of the hands in egocentric vision a survey"):
  with members:
    - "an effective and efficient method for detecting hands in egocentric videos for rehabilitation applications"
    - "recurrent u net for resource constrained segmentation"
    - "h o unified egocentric recognition of 3d hand object poses and interactions"
    - "domain specific priors and meta learning for low shot first person action recognition"
    - "egocentric hand track and object based human action recognition"
    - "egocentric video a new tool for capturing hand use of individuals with spinal cord injury at home"
    - "scaling egocentric vision the epic kitchens dataset"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-2 (induced by source paper "pushing the envelope for rgb based dense 3d hand pose estimation via neural rendering"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "deephps end to end estimation of 3d hand pose and shape by learning from synthetic depth"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "cross modal deep variational hand pose estimation"
    - "densepose dense human pose estimation in the wild"

Connected through hyperedge HE-3 (induced by source paper "recurrent u net for resource constrained segmentation"):
  with members:
    - "fast scnn fast semantic segmentation network"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "group normalization"
    - "contextnet exploring context and detail for semantic segmentation in real time"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "icnet for real time semantic segmentation on high resolution images"
    - "beyond the pixel wise loss for topology aware delineation"
    - "residual conv deconv grid network for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "an end to end framework for unconstrained monocular 3d hand pose estimation"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "learning joint reconstruction of hands and manipulated objects"
    - "3d hand shape and pose from images in the wild"
    - "single image 3d hand reconstruction with mesh convolutions"
    - "cross modal deep variational hand pose estimation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "using a single rgb frame for real time 3d hand pose estimation in the wild"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"

Connected through hyperedge HE-5 (induced by source paper "fingertip detection and tracking for recognition of air writing in videos"):
  with members:
    - "learning to estimate 3d hand pose from single rgb images"
    - "using a single rgb frame for real time 3d hand pose estimation in the wild"
    - "toward high performance online hccr"
    - "emnist an extension of mnist to handwritten letters"
    - "design of a very compact cnn classifier for online handwritten chinese character recognition using dropweight and global pooling"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "recognizing fine grained and composite activities using hand centric features and script data"
    - "dropsample a new training method to enhance deep convolutional neural networks for large scale unconstrained handwritten chinese character recognition"

Connected through hyperedge HE-6 (induced by source paper "busyhands a hand tool interaction database for assembly tasks semantic segmentation"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-7 (induced by source paper "learning task oriented grasping from human activity datasets"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "learning to estimate pose and shape of hand held objects from rgb images"
    - "learning joint reconstruction of hands and manipulated objects"
    - "h o unified egocentric recognition of 3d hand object poses and interactions"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "global search with bernoulli alternation kernel for task oriented grasping informed by simulation"
    - "learning to estimate 3d hand pose from single rgb images"

Connected through hyperedge HE-8 (induced by source paper "automatically extract the semi transparent motion blurred hand from a single image"):
  with members:
    - "tom net learning transparent object matting from a single image"
    - "semantic human matting"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "learning depth from single images with deep neural network embedding focal length"
    - "deep image matting"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

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)