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: "flow guided feature aggregation for video object detection"
  Abstract: Extending state-of-the-art object detectors from image to video is challenging. The accuracy of detection suffers from degenerated object appearances in videos, e.g., motion blur, video defocus, rare poses, etc. Existing work attempts to exploit temporal information on box level, but such methods are not trained end-to-end. We present flow-guided feature aggregation, an accurate and end-to-end learning framework for video object detection. It leverages temporal coherence on feature level instead. It improves the per-frame features by aggregation of nearby features along the motion paths, and thus improves the video recognition accuracy. Our method significantly improves upon strong single-frame baselines in ImageNet VID, especially for more challenging fast moving objects. Our framework is principled, and on par with the best engineered systems winning the ImageNet VID challenges 2016, without additional bells-and-whistles. The proposed method, together with Deep Feature Flow, powered the winning entry of ImageNet VID challenges 2017. The code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "mast a memory augmented self supervised tracker"):
  with members:
    - "video object segmentation using space time memory networks"
    - "self supervised learning for video correspondence flow"
    - "multigrid predictive filter flow for unsupervised learning on videos"
    - "learning correspondence from the cycle consistency of time"
    - "joint task self supervised learning for temporal correspondence"
    - "unsupervised deep tracking"
    - "rvos end to end recurrent network for video object segmentation"
    - "feelvos fast end to end embedding learning for video object segmentation"

Connected through hyperedge HE-2 (induced by source paper "joint anchor feature refinement for real time accurate object detection in images and videos"):
  with members:
    - "cascade retinanet maintaining consistency for single stage object detection"
    - "region proposal by guided anchoring"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "object detection in video with spatiotemporal sampling networks"
    - "towards high performance video object detection for mobiles"

Connected through hyperedge HE-3 (induced by source paper "plug play convolutional regression tracker for video object detection"):
  with members:
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "deep high resolution representation learning for human pose estimation"
    - "sequence level semantics aggregation for video object detection"
    - "relation distillation networks for video object detection"
    - "object detection in video with spatiotemporal sampling networks"
    - "optimizing video object detection via a scale time lattice"
    - "integrated object detection and tracking with tracklet conditioned detection"
    - "deformable convnets v2 more deformable better results"

Connected through hyperedge HE-4 (induced by source paper "animal detection in man made environments"):
  with members:
    - "looking fast and slow memory guided mobile video object detection"
    - "half a percent of labels is enough efficient animal detection in uav imagery using deep cnns and active learning"
    - "the iwildcam 2019 challenge dataset"
    - "searching for mobilenetv3"
    - "the via annotation software for images audio and video"
    - "large scale interactive object segmentation with human annotators"
    - "detecting mammals in uav images best practices to address a substantially imbalanced dataset with deep learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-5 (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"
    - "dynamic video segmentation network"
    - "low latency video semantic segmentation"
    - "generative image inpainting with contextual attention"
    - "skip connections eliminate singularities"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-6 (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"
    - "dynamic video segmentation network"
    - "low latency video semantic segmentation"
    - "light weight refinenet for real time semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "an empirical study of spatial attention mechanisms in deep networks"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "attention is not explanation"
    - "transformer xl attentive language models beyond a fixed length context"
    - "ocnet object context network for scene parsing"
    - "self attention with relative position representations"
    - "ccnet criss cross attention for semantic segmentation"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "learning region features for object detection"

Connected through hyperedge HE-8 (induced by source paper "relation distillation networks for video object detection"):
  with members:
    - "exploring object relation in mean teacher for cross domain detection"
    - "cornernet detecting objects as paired keypoints"
    - "object detection in video with spatiotemporal sampling networks"
    - "videos as space time region graphs"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "exploring visual relationship for image captioning"
    - "non local neural networks"
    - "towards high performance video object detection"

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)