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: "dynamic video segmentation network"
  Abstract: In this paper, we present a detailed design of dynamic video segmentation network (DVSNet) for fast and efficient semantic video segmentation. DVSNet consists of two convolutional neural networks: a segmentation network and a flow network. The former generates highly accurate semantic segmentations, but is deeper and slower. The latter is much faster than the former, but its output requires further processing to generate less accurate semantic segmentations. We explore the use of a decision network to adaptively assign different frame regions to different networks based on a metric called expected confidence score. Frame regions with a higher expected confidence score traverse the flow network. Frame regions with a lower expected confidence score have to pass through the segmentation network. We have extensively performed experiments on various configurations of DVSNet, and investigated a number of variants for the proposed decision network. The experimental results show that our DVSNet is able to achieve up to 70.4% mIoU at 19.8 fps on the Cityscape dataset. A high speed version of DVSNet is able to deliver an fps of 30.4 with 63.2% mIoU on the same dataset. DVSNet is also able to reduce up to 95\% of the computational workloads.

Connected through hyperedge HE-1 (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"
    - "deep spatio temporal random fields for efficient video segmentation"

Connected through hyperedge HE-2 (induced by source paper "veritatem dies aperit temporally consistent depth prediction enabled by a multi task geometric and semantic scene understanding approach"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "learning a discriminative feature network for semantic segmentation"
    - "deep depth completion of a single rgb d image"
    - "low latency video semantic segmentation"
    - "generative image inpainting with contextual attention"
    - "skip connections eliminate singularities"
    - "cycada cycle consistent adversarial domain adaptation"
    - "flow guided feature aggregation for video object detection"

Connected through hyperedge HE-3 (induced by source paper "dynamic face video segmentation via reinforcement learning"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "face mask extraction in video sequence"
    - "architecture search of dynamic cells for semantic video segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "meta learning transferable active learning policies by deep reinforcement learning"
    - "low latency video semantic segmentation"
    - "light weight refinenet for real time semantic segmentation"
    - "inter bmv interpolation with block motion vectors for fast semantic segmentation on video"

Connected through hyperedge HE-4 (induced by source paper "progressive sparse local attention for video object detection"):
  with members:
    - "ocnet object context network for scene parsing"
    - "ccnet criss cross attention for semantic segmentation"
    - "optimizing video object detection via a scale time lattice"
    - "low latency video semantic segmentation"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "dual attention network for scene segmentation"
    - "non local neural networks"
    - "towards high performance video object detection"

Connected through hyperedge HE-5 (induced by source paper "architecture search of dynamic cells for semantic video segmentation"):
  with members:
    - "deep spatio temporal random fields for efficient video segmentation"
    - "low latency video semantic segmentation"
    - "fast neural architecture search of compact semantic segmentation models via auxiliary cells"
    - "snas stochastic neural architecture search"
    - "light weight refinenet for real time semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "deformable convnets v2 more deformable better results"
    - "searching for efficient multi scale architectures for dense image prediction"

Connected through hyperedge HE-6 (induced by source paper "efficient video semantic segmentation with labels propagation and refinement"):
  with members:
    - "panoptic feature pyramid networks"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "low latency video semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "icnet for real time semantic segmentation on high resolution images"
    - "semantic video cnns through representation warping"
    - "learning video object segmentation with visual memory"

Connected through hyperedge HE-7 (induced by source paper "boosting real time driving scene parsing with shared semantics"):
  with members:
    - "short term prediction and multi camera fusion on semantic grids"
    - "sfnet learning object aware semantic correspondence"
    - "a lightweight optical flow cnn revisiting data fidelity and regularization"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "learning correspondence from the cycle consistency of time"
    - "searching for mobilenetv3"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"

Connected through hyperedge HE-8 (induced by source paper "semi supervised breast lesion detection in ultrasound video based on temporal coherence"):
  with members:
    - "yolov3 an incremental improvement"
    - "towards high performance video object detection"
    - "focal loss for dense object detection"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "deep feature flow for video recognition"
    - "high speed tracking with kernelized correlation filters"
    - "adam a method for stochastic optimization"

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)