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: "geometric context from videos"
  Abstract: We present a novel algorithm for estimating the broad 3D geometric structure of outdoor video scenes. Leveraging spatio-temporal video segmentation, we decompose a dynamic scene captured by a video into geometric classes, based on predictions made by region-classifiers that are trained on appearance and motion features. By examining the homogeneity of the prediction, we combine predictions across multiple segmentation hierarchy levels alleviating the need to determine the granularity a priori. We built a novel, extensive dataset on geometric context of video to evaluate our method, consisting of over 100 ground-truth annotated outdoor videos with over 20,000 frames. To further scale beyond this dataset, we propose a semi-supervised learning framework to expand the pool of labeled data with high confidence predictions obtained from unlabeled data. Our system produces an accurate prediction of geometric context of video achieving 96% accuracy across main geometric classes.

Connected through hyperedge HE-1 (induced by source paper "log densenet how to sparsify a densenet"):
  with members:
    - "dual path networks"
    - "anytime neural networks via joint optimization of auxiliary losses"
    - "deep networks with stochastic depth"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"
    - "deep roots improving cnn efficiency with hierarchical filter groups"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-2 (induced by source paper "stacked deconvolutional network for semantic segmentation"):
  with members:
    - "deformable convolutional networks"
    - "understanding convolution for semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "stacked hourglass networks for human pose estimation"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"
    - "wider or deeper revisiting the resnet model for visual recognition"

Connected through hyperedge HE-3 (induced by source paper "grab pay and eat semantic food detection for smart restaurants"):
  with members:
    - "food recognition using fusion of classifiers based on cnns"
    - "food ingredients recognition through multi label learning"
    - "exploring food detection using cnns"
    - "yolo9000 better faster stronger"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "wide slice residual networks for food recognition"

Connected through hyperedge HE-4 (induced by source paper "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"):
  with members:
    - "residual networks are exponential ensembles of relatively shallow networks"
    - "the importance of skip connections in biomedical image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "densely connected convolutional networks"
    - "playing for data ground truth from computer games"
    - "learning aligned cross modal representations from weakly aligned data"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-5 (induced by source paper "towards segmenting consumer stereo videos benchmark baselines and ensembles"):
  with members:
    - "efficient hierarchical graph based segmentation of rgbd videos"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "deep end2end voxel2voxel prediction"):
  with members:
    - "flow fields dense correspondence fields for highly accurate large displacement optical flow estimation"
    - "beyond short snippets deep networks for video classification"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "deepedge a multi scale bifurcated deep network for top down contour detection"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "depth map prediction from a single image using a multi scale deep network"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-7 (induced by source paper "a study of actor and action semantic retention in video supervoxel segmentation"):
  with members:
    - "imagenet large scale visual recognition challenge"

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)