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: "optimizing video object detection via a scale time lattice"
  Abstract: High-performance object detection relies on expensive convolutional networks to compute features, often leading to significant challenges in applications, e.g. those that require detecting objects from video streams in real time. The key to this problem is to trade accuracy for efficiency in an effective way, i.e. reducing the computing cost while maintaining competitive performance. To seek a good balance, previous efforts usually focus on optimizing the model architectures. This paper explores an alternative approach, that is, to reallocate the computation over a scale-time space. The basic idea is to perform expensive detection sparsely and propagate the results across both scales and time with substantially cheaper networks, by exploiting the strong correlations among them. Specifically, we present a unified framework that integrates detection, temporal propagation, and across-scale refinement on a Scale-Time Lattice. On this framework, one can explore various strategies to balance performance and cost. Taking advantage of this flexibility, we further develop an adaptive scheme with the detector invoked on demand and thus obtain improved tradeoff. On ImageNet VID dataset, the proposed method can achieve a competitive mAP 79.6% at 20 fps, or 79.0% at 62 fps as a performance/speed tradeoff.

Connected through hyperedge HE-1 (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"
    - "integrated object detection and tracking with tracklet conditioned detection"
    - "deformable convnets v2 more deformable better results"
    - "flow guided feature aggregation for video object detection"

Connected through hyperedge HE-2 (induced by source paper "looking fast and slow memory guided mobile video object detection"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "object detection in video with spatiotemporal sampling networks"
    - "depth adaptive computational policies for efficient visual tracking"
    - "towards high performance video object detection for mobiles"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "low latency video semantic segmentation"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-3 (induced by source paper "progressive sparse local attention for video object detection"):
  with members:
    - "ocnet object context network for scene parsing"
    - "dynamic video segmentation network"
    - "ccnet criss cross attention for semantic segmentation"
    - "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-4 (induced by source paper "sequence level semantics aggregation for video object detection"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "yolov3 an incremental improvement"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "videos as space time region graphs"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "non local neural networks"
    - "towards high performance video object detection"
    - "flow guided feature aggregation for video object detection"

Connected through hyperedge HE-5 (induced by source paper "object detection in video with spatial temporal context aggregation"):
  with members:
    - "universal transformers"
    - "object detection in video with spatiotemporal sampling networks"
    - "object detection in videos by high quality object linking"
    - "object detection in videos by short and long range object linking"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "non local neural networks"
    - "flow guided feature aggregation for video object detection"
    - "focal loss for dense object detection"

Connected through hyperedge HE-6 (induced by source paper "learning motion priors for efficient video object detection"):
  with members:
    - "towards high performance video object detection"
    - "flow guided feature aggregation for video object detection"
    - "focal loss for dense object detection"
    - "deformable convolutional networks"
    - "detect to track and track to detect"
    - "object detection with an aligned spatial temporal memory"
    - "object detection in videos with tubelet proposal networks"
    - "impression network for video object detection"

Connected through hyperedge HE-7 (induced by source paper "a delay metric for video object detection what average precision fails to tell"):
  with members:
    - "step spatio temporal progressive learning for video action detection"
    - "minimum delay object detection from video"
    - "catdet cascaded tracked detector for efficient object detection from video"
    - "object detection in video with spatiotemporal sampling networks"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "detect or track towards cost effective video object detection tracking"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-8 (induced by source paper "great ape detection in challenging jungle camera trap footage via attention based spatial and temporal feature blending"):
  with members:
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "distractor aware siamese networks for visual object tracking"
    - "siamrpn evolution of siamese visual tracking with very deep networks"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "flow guided feature aggregation for video object detection"
    - "focal loss for dense 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)