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: "see more know more unsupervised video object segmentation with co attention siamese networks"
  Abstract: We introduce a novel network, called CO-attention Siamese Network (COSNet), to address the unsupervised video object segmentation task from a holistic view. We emphasize the importance of inherent correlation among video frames and incorporate a global co-attention mechanism to improve further the state-of-the-art deep learning based solutions that primarily focus on learning discriminative foreground representations over appearance and motion in short-term temporal segments. The co-attention layers in our network provide efficient and competent stages for capturing global correlations and scene context by jointly computing and appending co-attention responses into a joint feature space. We train COSNet with pairs of video frames, which naturally augments training data and allows increased learning capacity. During the segmentation stage, the co-attention model encodes useful information by processing multiple reference frames together, which is leveraged to infer the frequently reappearing and salient foreground objects better. We propose a unified and end-to-end trainable framework where different co-attention variants can be derived for mining the rich context within videos. Our extensive experiments over three large benchmarks manifest that COSNet outperforms the current alternatives by a large margin.

Connected through hyperedge HE-1 (induced by source paper "zero shot video object segmentation via attentive graph neural networks"):
  with members:
    - "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"
    - "videos as space time region graphs"

Connected through hyperedge HE-2 (induced by source paper "human aware motion deblurring"):
  with members:
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "gated fusion network for single image dehazing"
    - "scale recurrent network for deep image deblurring"
    - "external prior guided internal prior learning for real world noisy image denoising"
    - "image correction via deep reciprocating hdr transformation"
    - "deep semantic face deblurring"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "learning blind motion deblurring"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised few shot object segmentation using co attention with visual and semantic inputs"):
  with members:
    - "one shot object detection with co attention and co excitation"
    - "online object representations with contrastive learning"
    - "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"
    - "youtube vos a large scale video object segmentation benchmark"
    - "online object and task learning via human robot interaction"
    - "one shot learning for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "motion attentive transition for zero shot video object segmentation"):
  with members:
    - "zero shot video object segmentation via attentive graph neural networks"
    - "rvos end to end recurrent network for video object segmentation"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "salient objects in clutter bringing salient object detection to the foreground"
    - "video object segmentation using teacher student adaptation in a human robot interaction hri setting"
    - "cbam convolutional block attention module"
    - "learning to segment moving objects"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"

Connected through hyperedge HE-5 (induced by source paper "cagnet content aware guidance for salient object detection"):
  with members:
    - "connnet a long range relation aware pixel connectivity network for salient segmentation"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "deeply supervised salient object detection with short connections"
    - "saliency for fine grained object recognition in domains with scarce training data"
    - "learning a discriminative feature network for semantic segmentation"
    - "salient objects in clutter bringing salient object detection to the foreground"
    - "probabilistic saliency estimation"
    - "video salient object detection via fully convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "looking at the right stuff guided semantic gaze for autonomous driving"):
  with members:
    - "attention based dropout layer for weakly supervised object localization"
    - "dynamic traffic scene classification with space time coherence"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "salient object detection a survey"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "yolov3 an incremental improvement"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"

Connected through hyperedge HE-7 (induced by source paper "one shot weakly supervised video object segmentation"):
  with members:
    - "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"
    - "few shot image recognition by predicting parameters from activations"

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)