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: "fusionseg learning to combine motion and appearance for fully automatic segmention of generic objects in videos"
  Abstract: We propose an end-to-end learning framework for segmenting generic objects in videos. Our method learns to combine appearance and motion information to produce pixel level segmentation masks for all prominent objects in videos. We formulate this task as a structured prediction problem and design a two-stream fully convolutional neural network which fuses together motion and appearance in a unified framework. Since large-scale video datasets with pixel level segmentations are problematic, we show how to bootstrap weakly annotated videos together with existing image recognition datasets for training. Through experiments on three challenging video segmentation benchmarks, our method substantially improves the state-of-the-art for segmenting generic (unseen) objects. Code and pre-trained models are available on the project website.

Connected through hyperedge HE-1 (induced by source paper "adaptive masked proxies for few shot segmentation"):
  with members:
    - "three scenarios for continual learning"
    - "deep clustering for unsupervised learning of visual features"
    - "sg one similarity guidance network for one shot semantic segmentation"
    - "video object segmentation using teacher student adaptation in a human robot interaction hri setting"
    - "prototypical networks for few shot learning"
    - "few shot image recognition by predicting parameters from activations"
    - "no fuss distance metric learning using proxies"
    - "transfer of view manifold learning to similarity perception of novel objects"

Connected through hyperedge HE-2 (induced by source paper "exploiting geometric constraints on dense trajectories for motion saliency"):
  with members:
    - "revisiting video saliency a large scale benchmark and a new model"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "videomatch matching based video object segmentation"
    - "blazingly fast video object segmentation with pixel wise metric learning"
    - "video object segmentation using teacher student adaptation in a human robot interaction hri setting"
    - "the 2018 davis challenge on video object segmentation"
    - "learning to segment moving objects"

Connected through hyperedge HE-3 (induced by source paper "one shot weakly supervised video object segmentation"):
  with members:
    - "see more know more unsupervised video object segmentation with co attention siamese networks"
    - "adaptive masked proxies for few shot segmentation"
    - "panet few shot image semantic segmentation with prototype alignment"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"
    - "video object segmentation with language referring expressions"
    - "youtube vos a large scale video object segmentation benchmark"
    - "premvos proposal generation refinement and merging for video object segmentation"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-4 (induced by source paper "optical flow augmented semantic segmentation networks for automated driving"):
  with members:
    - "semantic video cnns through representation warping"
    - "sfm net learning of structure and motion from video"
    - "modnet moving object detection network with motion and appearance for autonomous driving"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "learning motion patterns in videos"
    - "semantic video segmentation by gated recurrent flow propagation"
    - "virtual worlds as proxy for multi object tracking analysis"

Connected through hyperedge HE-5 (induced by source paper "spacetime graph optimization for video object segmentation"):
  with members:
    - "unsupervised online video object segmentation with motion property understanding"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "blazingly fast video object segmentation with pixel wise metric learning"
    - "unsupervised object segmentation in video by efficient selection of highly probable positive features"
    - "unsupervised learning from video to detect foreground objects in single images"
    - "video object segmentation without temporal information"

Connected through hyperedge HE-6 (induced by source paper "a spectral approach to unsupervised object segmentation in video"):
  with members:
    - "fast and accurate online video object segmentation via tracking parts"
    - "efficient video object segmentation via network modulation"
    - "learning video object segmentation with visual memory"
    - "online adaptation of convolutional neural networks for video object segmentation"
    - "segflow joint learning for video object segmentation and optical flow"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "one shot video object segmentation"
    - "efficient hierarchical graph based segmentation of rgbd videos"

Connected through hyperedge HE-7 (induced by source paper "fisheyemodnet moving object detection on surround view cameras for autonomous driving"):
  with members:
    - "multinet multi stream feature aggregation and geometric loss strategy for multi task learning"
    - "woodscape a multi task multi camera fisheye dataset for autonomous driving"
    - "neurall towards a unified model for visual perception in automated driving"
    - "rtseg real time semantic segmentation comparative study"
    - "shuffleseg real time semantic segmentation network"

Connected through hyperedge HE-8 (induced by source paper "msnet mutual suppression network for disentangled video representations"):
  with members:
    - "decomposing motion and content for natural video sequence prediction"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "folded recurrent neural networks for future video prediction"
    - "unsupervised learning of disentangled representations from video"
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes"
    - "spatio temporal action detection with cascade proposal and location anticipation"
    - "an end to end 3d convolutional neural network for action detection and segmentation in videos"

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)