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: "flight dynamics based recovery of a uav trajectory using ground cameras"
  Abstract: We propose a new method to estimate the 6-dof trajectory of a flying object such as a quadrotor UAV within a 3D airspace monitored using multiple fixed ground cameras. It is based on a new structure from motion formulation for the 3D reconstruction of a single moving point with known motion dynamics. Our main contribution is a new bundle adjustment procedure, which in addition to optimizing the camera poses, regularizes the point trajectory using a prior based on motion dynamics (or specifically flight dynamics). Furthermore, we can infer the underlying control input sent to the UAVs autopilot that determined its flight trajectory. Our method requires neither perfect single-view tracking nor appearance matching across views. For robustness, we allow the tracker to generate multiple detections per frame in each video. The true detections and the data association across videos is estimated using robust multi-view triangulation and subsequently refined in our bundle adjustment formulation. Quantitative evaluation on simulated data and experiments on real videos from indoor and outdoor scenes shows that our technique is superior to existing methods.

Connected through hyperedge HE-1 (induced by source paper "reconstruction of 3d flight trajectories from ad hoc camera networks"):
  with members:
    - "vision based real time aerial object localization and tracking for uav sensing system"
    - "geometric models of rolling shutter cameras"

Connected through hyperedge HE-2 (induced by source paper "sok security and privacy in the age of drones threats challenges solution mechanisms and scientific gaps"):
  with members:
    - "game of drones detecting streamed poi from encrypted fpv channel"
    - "dolphinattack inaudible voice commands"
    - "using deep networks for drone detection"
    - "dr0wned cyber physical attack with additive manufacturing"
    - "flying objects detection from a single moving camera"

Connected through hyperedge HE-3 (induced by source paper "pinch an effective efficient and robust solution to drone detection via network traffic analysis"):
  with members:
    - "machine learning based delay aware uav detection and operation mode identification over encrypted wi fi traffic"
    - "key technologies and system trade offs for detection and localization of amateur drones"
    - "device fingerprinting in wireless networks challenges and opportunities"

Connected through hyperedge HE-4 (induced by source paper "automatic adaptation of person association for multiview tracking in group activities"):
  with members:
    - "learning deep context aware features over body and latent parts for person re identification"
    - "stacked hourglass networks for human pose estimation"
    - "coarse to fine volumetric prediction for single image 3d human pose"
    - "mot16 a benchmark for multi object tracking"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "performance measures and a data set for multi target multi camera tracking"
    - "3d human pose estimation from a single image via distance matrix regression"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-5 (induced by source paper "key technologies and system trade offs for detection and localization of amateur drones"):
  with members:
    - "electrosense open and big spectrum data"
    - "ultra reliable uav communication using altitude and cooperation diversity"
    - "deep architectures for modulation recognition"
    - "localization in long range ultra narrow band iot networks using rssi"
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"
    - "convolutional radio modulation recognition networks"

Connected through hyperedge HE-6 (induced by source paper "estimating metric poses of dynamic objects using monocular visual inertial fusion"):
  with members:
    - "6 dof object pose from semantic keypoints"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"

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)