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: "local histogram matching for efficient optical flow computation applied to velocity estimation on pocket drones"
  Abstract: Autonomous flight of pocket drones is challenging due to the severe limitations on on-board energy, sensing, and processing power. However, tiny drones have great potential as their small size allows maneuvering through narrow spaces while their small weight provides significant safety advantages. This paper presents a computationally efficient algorithm for determining optical flow, which can be run on an STM32F4 microprocessor (168 MHz) of a 4 gram stereo-camera. The optical flow algorithm is based on edge histograms. We propose a matching scheme to determine local optical flow. Moreover, the method allows for sub-pixel flow determination based on time horizon adaptation. We demonstrate velocity measurements in flight and use it within a velocity control-loop on a pocket drone.

Connected through hyperedge HE-1 (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"
    - "efficient optical flow and stereo vision for velocity estimation and obstacle avoidance on an autonomous pocket drone"
    - "cad2rl real single image flight without a single real image"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (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"
    - "efficient optical flow and stereo vision for velocity estimation and obstacle avoidance on an autonomous pocket drone"
    - "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-3 (induced by source paper "efficient optical flow and stereo vision for velocity estimation and obstacle avoidance on an autonomous pocket drone"):
  with members:
    - "obstacle avoidance strategy using onboard stereo vision on a flapping wing mav"

Connected through hyperedge HE-4 (induced by source paper "on board communication based relative localization for collision avoidance in micro air vehicle teams"):
  with members:
    - "efficient optical flow and stereo vision for velocity estimation and obstacle avoidance on an autonomous pocket drone"
    - "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)