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: "mobile video object detection with temporally aware feature maps"
  Abstract: This paper introduces an online model for object detection in videos designed to run in real-time on low-powered mobile and embedded devices. Our approach combines fast single-image object detection with convolutional long short term memory (LSTM) layers to create an interweaved recurrent-convolutional architecture. Additionally, we propose an efficient Bottleneck-LSTM layer that significantly reduces computational cost compared to regular LSTMs. Our network achieves temporal awareness by using Bottleneck-LSTMs to refine and propagate feature maps across frames. This approach is substantially faster than existing detection methods in video, outperforming the fastest single-frame models in model size and computational cost while attaining accuracy comparable to much more expensive single-frame models on the Imagenet VID 2015 dataset. Our model reaches a real-time inference speed of up to 15 FPS on a mobile CPU.

Connected through hyperedge HE-1 (induced by source paper "fast object detection in compressed video"):
  with members:
    - "object detection in video with spatiotemporal sampling networks"
    - "group normalization"
    - "towards high performance video object detection for mobiles"
    - "optimizing video object detection via a scale time lattice"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "mv yolo motion vector aided tracking by semantic object detection"
    - "learning transferable architectures for scalable image recognition"
    - "towards high performance video object detection"

Connected through hyperedge HE-2 (induced by source paper "towards high performance video object detection for mobiles"):
  with members:
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "tiny ssd a tiny single shot detection deep convolutional neural network for real time embedded object detection"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "fast yolo a fast you only look once system for real time embedded object detection in video"
    - "towards high performance video object detection"
    - "flow guided feature aggregation for video object detection"
    - "focal loss for dense object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-3 (induced by source paper "small scale pedestrian detection based on somatic topology localization and temporal feature aggregation"):
  with members:
    - "too far to see not really pedestrian detection with scale aware localization policy"
    - "flow guided feature aggregation for video object detection"
    - "citypersons a diverse dataset for pedestrian detection"
    - "repulsion loss detecting pedestrians in a crowd"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "fused dnn a deep neural network fusion approach to fast and robust pedestrian detection"
    - "object detection from video tubelets with convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised convolutional lstm approach for tool tracking in laparoscopic videos"):
  with members:
    - "weakly supervised instance segmentation using class peak response"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "weakly supervised learning for tool localization in laparoscopic videos"
    - "deepphase surgical phase recognition in cataracts videos"
    - "tool detection and operative skill assessment in surgical videos using region based convolutional neural networks"
    - "simultaneous recognition and pose estimation of instruments in minimally invasive surgery"
    - "constrained deep weak supervision for histopathology image segmentation"
    - "hide and seek forcing a network to be meticulous for weakly supervised object and action localization"

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)