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: "efficient optical flow and stereo vision for velocity estimation and obstacle avoidance on an autonomous pocket drone"
  Abstract: Micro Aerial Vehicles (FOV) are very suitable for flying in indoor environments, but autonomous navigation is challenging due to their strict hardware limitations. This paper presents a highly efficient computer vision algorithm called Edge-FS for the determination of velocity and depth. It runs at 20 Hz on a 4 g stereo camera with an embedded STM32F4 microprocessor (168 MHz, 192 kB) and uses edge distributions to calculate optical flow and stereo disparity. The stereo-based distance estimates are used to scale the optical flow in order to retrieve the drone's velocity. The velocity and depth measurements are used for fully autonomous flight of a 40 g pocket drone only relying on on-board sensors. This method allows the MAV to control its velocity and avoid obstacles.

Connected through hyperedge HE-1 (induced by source paper "hundred drones land in a minute"):
  with members:
    - "massive mimo for communications with drone swarms"

Connected through hyperedge HE-2 (induced by source paper "object segmentation tracking from generic video cues"):
  with members:
    - "fast and accurate online video object segmentation via tracking parts"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "fast online object tracking and segmentation a unifying approach"
    - "video object segmentation using teacher student adaptation in a human robot interaction hri setting"
    - "pixel level matching for video object segmentation using convolutional neural networks"
    - "the 2017 davis challenge on video object segmentation"

Connected through hyperedge HE-3 (induced by source paper "a 64 mw dnn based visual navigation engine for autonomous nano drones"):
  with members:
    - "cmsis nn efficient neural network kernels for arm cortex m cpus"
    - "an iot endpoint system on chip for secure and energy efficient near sensor analytics"
    - "learning to fly by crashing"
    - "aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision"
    - "cad2rl real single image flight without a single real image"
    - "local histogram matching for efficient optical flow computation applied to velocity estimation on pocket drones"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "an open source and open hardware deep learning powered visual navigation engine for autonomous nano uavs"):
  with members:
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "local histogram matching for efficient optical flow computation applied to velocity estimation on pocket drones"
    - "end to end learning of driving models from large scale video datasets"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "deep residual learning for image recognition"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-5 (induced by source paper "visual model predictive localization for computationally efficient autonomous racing of a 72 gram drone"):
  with members:
    - "deep drone racing learning agile flight in dynamic environments"
    - "gapflyt active vision based minimalist structure less gap detection for quadrotor flight"
    - "autonomous drone race a computationally efficient vision based navigation and control strategy"
    - "beauty and the beast optimal methods meet learning for drone racing"
    - "aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision"

Connected through hyperedge HE-6 (induced by source paper "end to end collision avoidance based on optical flow and neural networks"):
  with members:
    - "gapflyt active vision based minimalist structure less gap detection for quadrotor flight"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "wind gust detection using physical sensors in quadcopters"):
  with members:
    - "learning monocular reactive uav control in cluttered natural environments"

Connected through hyperedge HE-8 (induced by source paper "on board communication based relative localization for collision avoidance in micro air vehicle teams"):
  with members:
    - "local histogram matching for efficient optical flow computation applied to velocity estimation on pocket drones"
    - "optical flow based self supervised learning of obstacle appearance applied to mav landing"
    - "3 d reciprocal collision avoidance on physical quadrotor helicopters with on board sensing for relative positioning"
    - "outdoor flocking and formation flight with autonomous aerial robots"

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)