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: "towards high performance video object detection for mobiles"
  Abstract: Despite the recent success of video object detection on Desktop GPUs, its architecture is still far too heavy for mobiles. It is also unclear whether the key principles of sparse feature propagation and multi-frame feature aggregation apply at very limited computational resources. In this paper, we present a light weight network architecture for video object detection on mobiles. Light weight image object detector is applied on sparse key frames. A very small network, Light Flow, is designed for establishing correspondence across frames. A flow-guided GRU module is designed to effectively aggregate features on key frames. For non-key frames, sparse feature propagation is performed. The whole network can be trained end-to-end. The proposed system achieves 60.2% mAP score at speed of 25.6 fps on mobiles (e.g., HuaWei Mate 8).

Connected through hyperedge HE-1 (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"
    - "yolov3 an incremental improvement"

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"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "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"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-3 (induced by source paper "patchwork a patch wise attention network for efficient object detection and segmentation in video streams"):
  with members:
    - "object detection in video with spatiotemporal sampling networks"
    - "low latency video semantic segmentation"
    - "towards high performance video object detection"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "detect to track and track to detect"
    - "learning video object segmentation with visual memory"
    - "segflow joint learning for video object segmentation and optical flow"
    - "object detection with an aligned spatial temporal memory"

Connected through hyperedge HE-4 (induced by source paper "deep learning acceleration techniques for real time mobile vision applications"):
  with members:
    - "convolutional neural network for intrusion detection system in cyber physical systems"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "ai benchmark running deep neural networks on android smartphones"
    - "towards a theoretical understanding of hashing based neural nets"
    - "tiny ssd a tiny single shot detection deep convolutional neural network for real time embedded object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep reinforcement learning for visual object tracking in videos"
    - "an efficient deep learning hashing neural network for mobile visual search"

Connected through hyperedge HE-5 (induced by source paper "recurrent mvsnet for high resolution multi view stereo depth inference"):
  with members:
    - "deepmvs learning multi view stereopsis"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "pyramid stereo matching network"
    - "learning a multi view stereo machine"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "surfacenet an end to end 3d neural network for multiview stereopsis"
    - "learned multi patch similarity"
    - "octnet learning deep 3d representations at high resolutions"

Connected through hyperedge HE-6 (induced by source paper "hmm guided frame querying for bandwidth constrained video search"):
  with members:
    - "adascale towards real time video object detection using adaptive scaling"
    - "scaling video analytics on constrained edge nodes"
    - "object detection in video with spatiotemporal sampling networks"
    - "on calibration of modern neural networks"
    - "towards high performance video object detection"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "fast object detection in compressed video"):
  with members:
    - "object detection in video with spatiotemporal sampling networks"
    - "group normalization"
    - "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"
    - "flow guided feature aggregation for 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)