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: "mv yolo motion vector aided tracking by semantic object detection"
  Abstract: Object tracking is the cornerstone of many visual analytics systems. While considerable progress has been made in this area in recent years, robust, efficient, and accurate tracking in real-world video remains a challenge. In this paper, we present a hybrid tracker that leverages motion information from the compressed video stream and a general-purpose semantic object detector acting on decoded frames to construct a fast and efficient tracking engine. The proposed approach is compared with several well-known recent trackers on the OTB tracking dataset. The results indicate advantages of the proposed method in terms of speed and/or accuracy.Other desirable features of the proposed method are its simplicity and deployment efficiency, which stems from the fact that it reuses the resources and information that may already exist in the system for other reasons.

Connected through hyperedge HE-1 (induced by source paper "towards automated swimming analytics using deep neural networks"):
  with members:
    - "yolov3 an incremental improvement"
    - "continuous video to simple signals for swimming stroke detection with convolutional neural networks"
    - "simple online and realtime tracking"

Connected through hyperedge HE-2 (induced by source paper "motion nets 6d tracking of unknown objects in unseen environments using rgb"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "a survey on deep transfer learning"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "on the continuity of rotation representations in neural networks"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "se3 pose nets structured deep dynamics models for visuomotor planning and control"
    - "real time seamless single shot 6d object pose prediction"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"

Connected through hyperedge HE-3 (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"
    - "learning transferable architectures for scalable image recognition"
    - "towards high performance video object detection"
    - "flow guided feature aggregation for video object detection"

Connected through hyperedge HE-4 (induced by source paper "enabling pedestrian safety using computer vision techniques a case study of the 2018 uber inc self driving car crash"):
  with members:
    - "yolov3 an incremental improvement"
    - "msr net low light image enhancement using deep convolutional network"
    - "focal loss for dense object detection"
    - "deep bilateral learning for real time image enhancement"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "dehazenet an end to end system for single image haze removal"
    - "multinet real time joint semantic reasoning for autonomous driving"
    - "fused dnn a deep neural network fusion approach to fast and robust pedestrian 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)