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: "segflow joint learning for video object segmentation and optical flow"
  Abstract: This paper proposes an end-to-end trainable network, SegFlow, for simultaneously predicting pixel-wise object segmentation and optical flow in videos. The proposed SegFlow has two branches where useful information of object segmentation and optical flow is propagated bidirectionally in a unified framework. The segmentation branch is based on a fully convolutional network, which has been proved effective in image segmentation task, and the optical flow branch takes advantage of the FlowNet model. The unified framework is trained iteratively offline to learn a generic notion, and fine-tuned online for specific objects. Extensive experiments on both the video object segmentation and optical flow datasets demonstrate that introducing optical flow improves the performance of segmentation and vice versa, against the state-of-the-art algorithms.

Connected through hyperedge HE-1 (induced by source paper "learning video object segmentation from unlabeled videos"):
  with members:
    - "zero shot video object segmentation via attentive graph neural networks"
    - "motion attentive transition for zero shot video object segmentation"
    - "self supervised learning for video correspondence flow"
    - "the 2019 davis challenge on vos unsupervised multi object segmentation"
    - "frame to frame aggregation of active regions in web videos for weakly supervised semantic segmentation"
    - "multigrid predictive filter flow for unsupervised learning on videos"
    - "multi source weak supervision for saliency detection"
    - "learning correspondence from the cycle consistency of time"

Connected through hyperedge HE-2 (induced by source paper "see more know more unsupervised video object segmentation with co attention siamese networks"):
  with members:
    - "deep visual attention prediction"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "videomatch matching based video object segmentation"
    - "pairwise body part attention for recognizing human object interactions"

Connected through hyperedge HE-3 (induced by source paper "dual temporal memory network for efficient video object segmentation"):
  with members:
    - "mhp vos multiple hypotheses propagation for video object segmentation"
    - "video object segmentation using space time memory networks"
    - "capsulevos semi supervised video object segmentation using capsule routing"
    - "spatiotemporal cnn for video object segmentation"
    - "lip learning instance propagation for video object segmentation"
    - "rvos end to end recurrent network for video object segmentation"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "ranet ranking attention network for fast video object segmentation"

Connected through hyperedge HE-4 (induced by source paper "efficient semantic video segmentation with per frame inference"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "deep video inpainting"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "high resolution representations for labeling pixels and regions"
    - "deep high resolution representation learning for human pose estimation"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "dynamic video segmentation network"

Connected through hyperedge HE-5 (induced by source paper "zero shot video object segmentation via attentive graph neural networks"):
  with members:
    - "see more know more unsupervised video object segmentation with co attention siamese networks"
    - "reasoning visual dialogs with structural and partial observations"
    - "rvos end to end recurrent network for video object segmentation"
    - "ranet ranking attention network for fast video object segmentation"
    - "semantic aware attention based deep object co segmentation"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "learning human object interactions by graph parsing neural networks"

Connected through hyperedge HE-6 (induced by source paper "joint learning of instance and semantic segmentation for robotic pick and place with heavy occlusions in clutter"):
  with members:
    - "probabilistic 3d multilabel real time mapping for multi object manipulation"
    - "instance segmentation of visible and occluded regions for finding and picking target from a pile of objects"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "joint learning of intrinsic images and semantic segmentation"
    - "fast object learning and dual arm coordination for cluttered stowing picking and packing"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "mask r cnn"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-7 (induced by source paper "directional deep embedding and appearance learning for fast video object segmentation"):
  with members:
    - "deeper and wider siamese networks for real time visual tracking"
    - "rvos end to end recurrent network for video object segmentation"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "atom accurate tracking by overlap maximization"
    - "fast and accurate online video object segmentation via tracking parts"
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"

Connected through hyperedge HE-8 (induced by source paper "fast video object segmentation using the global context module"):
  with members:
    - "video object segmentation using space time memory networks"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "ranet ranking attention network for fast video object segmentation"
    - "fast and accurate online video object segmentation via tracking parts"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "a generative appearance model for end to end video object segmentation"
    - "videomatch matching based video object 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)