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: "semantic video segmentation exploring inference efficiency"
  Abstract: We explore the efficiency of the CRF inference beyond image level semantic segmentation and perform joint inference in video frames. The key idea is to combine best of two worlds: semantic co-labeling and more expressive models. Our formulation enables us to perform inference over ten thousand images within seconds and makes the system amenable to perform video semantic segmentation most effectively. On CamVid dataset, with TextonBoost unaries, our proposed method achieves up to 8% improvement in accuracy over individual semantic image segmentation without additional time overhead. The source code is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "face mask extraction in video sequence"):
  with members:
    - "bringing background into the foreground making all classes equal in weakly supervised video semantic segmentation"
    - "end to end semantic face segmentation with conditional random fields as convolutional recurrent and adversarial networks"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "clockwork convnets for video semantic segmentation"
    - "one shot video object segmentation"
    - "semi supervised domain adaptation for weakly labeled semantic video object segmentation"
    - "visual speech recognition using pca networks and lstms in a tandem gmm hmm system"

Connected through hyperedge HE-2 (induced by source paper "semantic segmentation via highly fused convolutional network with multiple soft cost functions"):
  with members:
    - "a review on deep learning techniques applied to semantic segmentation"
    - "semantic understanding of scenes through the ade20k dataset"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "densely connected convolutional networks"
    - "video propagation networks"
    - "pyramid scene parsing network"
    - "r fcn object detection via region based fully convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "bringing background into the foreground making all classes equal in weakly supervised video semantic segmentation"):
  with members:
    - "weakly supervised semantic segmentation using web crawled videos"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "exploiting saliency for object segmentation from image level labels"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "bottom up top down cues for weakly supervised semantic segmentation"
    - "weakly supervised semantic segmentation using motion cues"
    - "video scene parsing with predictive feature learning"
    - "seed expand and constrain three principles for weakly supervised image segmentation"

Connected through hyperedge HE-4 (induced by source paper "semantic video cnns through representation warping"):
  with members:
    - "understanding convolution for semantic segmentation"
    - "fast optical flow using dense inverse search"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "joint optical flow and temporally consistent semantic segmentation"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "video propagation networks"
    - "clockwork convnets for video semantic segmentation"
    - "temporal segment networks towards good practices for deep action recognition"

Connected through hyperedge HE-5 (induced by source paper "video semantic object segmentation by self adaptation of dcnn"):
  with members:
    - "one shot video object segmentation"
    - "semantic image segmentation via deep parsing network"
    - "conditional random fields as recurrent neural networks"
    - "deep residual learning for image recognition"
    - "analysing domain shift factors between videos and images for object detection"
    - "learning deconvolution network for semantic segmentation"
    - "going deeper with convolutions"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"

Connected through hyperedge HE-6 (induced by source paper "semantic video segmentation by gated recurrent flow propagation"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "semantic video cnns through representation warping"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "std2p rgbd semantic segmentation using spatio temporal data driven pooling"
    - "video scene parsing with predictive feature learning"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "video propagation networks"

Connected through hyperedge HE-7 (induced by source paper "video propagation networks"):
  with members:
    - "fast optical flow using dense inverse search"
    - "learning video object segmentation from static images"
    - "clockwork convnets for video semantic segmentation"
    - "one shot video object segmentation"
    - "playing for data ground truth from computer games"
    - "learning sparse high dimensional filters image filtering dense crfs and bilateral neural networks"
    - "conditional random fields as recurrent neural networks"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-8 (induced by source paper "deep learning markov random field for semantic segmentation"):
  with members:
    - "video frame synthesis using deep voxel flow"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "semantic image segmentation via deep parsing network"
    - "conditional random fields as recurrent neural networks"
    - "multi scale context aggregation by dilated convolutions"
    - "fully connected deep structured networks"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"

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)