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: "on board visual tracking with unmanned aircraft system uas"
  Abstract: This paper presents the development of a real time tracking algorithm that runs on a 1.2 GHz PC/104 computer on-board a small UAV. The algorithm uses zero mean normalized cross correlation to detect and locate an object in the image. A kalman filter is used to make the tracking algorithm computationally efficient. Object position in an image frame is predicted using the motion model and a search window, centered at the predicted position is generated. Object position is updated with the measurement from object detection. The detected position is sent to the motion controller to move the gimbal so that the object stays at the center of the image frame. Detection and tracking is autonomously carried out on the payload computer and the system is able to work in two different methods. The first method starts detecting and tracking using a stored image patch. The second method allows the operator on the ground to select the interest object for the UAV to track. The system is capable of re-detecting an object, in the event of tracking failure. Performance of the tracking system was verified both in the lab and on the field by mounting the payload on a vehicle and simulating a flight. Tests show that the system can detect and track a diverse set of objects in real time. Flight testing of the system will be conducted at the next available opportunity.

Connected through hyperedge HE-1 (induced by source paper "sim4cv a photo realistic simulator for computer vision applications"):
  with members:
    - "carla an open urban driving simulator"
    - "end to end learning for self driving cars"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "asynchronous methods for deep reinforcement learning"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "learning physical intuition of block towers by example"
    - "playing for data ground truth from computer games"
    - "procedural generation of videos to train deep action recognition networks"

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)