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: "fast autonomous flight in warehouses for inventory applications"
  Abstract: The past years have shown a remarkable growth in use-cases for micro aerial vehicles (MAVs). Conceivable indoor applications require highly robust environment perception, fast reaction to changing situations, and stable navigation, but reliable sources of absolute positioning like GNSS or compass measurements are unavailable during indoor flights. We present a high-performance autonomous inventory MAV for operation inside warehouses. The MAV navigates along warehouse aisles and detects the placed stock in the shelves alongside its path with a multimodal sensor setup containing an RFID reader and two high-resolution cameras. We describe in detail the SLAM pipeline based on a 3D lidar, the setup for stock recognition, the mission planning and trajectory generation, as well as a low-level routine for avoidance of dynamical or previously unobserved obstacles. Experiments were performed in an operative warehouse of a logistics provider, in which an external warehouse management system provided the MAV with high-level inspection missions that are executed fully autonomously.

Connected through hyperedge HE-1 (induced by source paper "a 3d reactive navigation algorithm for mobile robots by using tentacle based sampling"):
  with members:
    - "autonomous navigation of mavs in unknown cluttered environments"
    - "a complete system for vision based micro aerial vehicle mapping planning and flight in cluttered environments"
    - "fast autonomous flight in gps denied and cluttered environments"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-2 (induced by source paper "visual perception and modelling in unstructured orchard for apple harvesting robots"):
  with members:
    - "single pixel reconstruction for one stage instance segmentation"
    - "fruit detection segmentation and 3d visualisation of environments in apple orchards"
    - "yolov3 an incremental improvement"
    - "deep learning in agriculture a survey"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "mask r cnn"
    - "deep fruit detection in orchards"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-3 (induced by source paper "detection and tracking of small objects in sparse 3d laser range data"):
  with members:
    - "efficient continuous time slam for 3d lidar based online mapping"
    - "team nimbro at mbzirc 2017 autonomous valve stem turning using a wrench"
    - "online visual robot tracking and identification using deep lstm networks"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "online multi target tracking using recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "fast time optimal avoidance of moving obstacles for high speed mav flight"):
  with members:
    - "search based motion planning for aggressive flight in se 3"
    - "search based motion planning for quadrotors using linear quadratic minimum time control"

Connected through hyperedge HE-5 (induced by source paper "search based 3d planning and trajectory optimization for safe micro aerial vehicle flight under sensor visibility constraints"):
  with members:
    - "funnel libraries for real time robust feedback motion planning"

Connected through hyperedge HE-6 (induced by source paper "team nimbro at mbzirc 2017 fast landing on a moving target and treasure hunting with a team of mavs"):
  with members:
    - "nimbro rescue solving disaster response tasks through mobile manipulation robot momaro"
    - "a decentralized multi agent unmanned aerial system to search pick up and relocate objects"
    - "the eth mav team in the mbz international robotics 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.RO(Robotics)