human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a 64 mw dnn based visual navigation engine for autonomous nano drones"):
  Abstract: Fully miniaturized robots (e.g., drones), with artificial intelligence (AI)-based visual navigation capabilities, are extremely challenging drivers of Internet-of-Things edge intelligence capabilities. Visual navigation based on AI approaches, such as deep neural networks (DNNs) are becoming pervasive for standard-size drones, but are considered out of reach for nano-drones with a size of a few cm2. In this paper, we present the first (to the best of our knowledge) demonstration of a navigation engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based visual navigation. To achieve this goal we developed a complete methodology for parallel execution of complex DNNs directly on board resource-constrained milliwatt-scale nodes. Our system is based on GAP8, a novel parallel ultralow-power computing platform, and a 27-g commercial, open-source Crazyflie 2.0 nano-quadrotor. As part of our general methodology, we discuss the software mapping techniques that enable the DroNet state-of-the-art deep convolutional neural network to be fully executed aboard within a strict 6 frame-per-second real-time constraint with no compromise in terms of flight results, while all processing is done with only 64 mW on average. Our navigation engine is flexible and can be used to span a wide performance range: at its peak performance corner, it achieves 18 frames/s while still consuming on average just 3.5% of the power envelope of the deployed nano-aircraft. To share our key findings with the embedded and robotics communities and foster further developments in autonomous nano-unmanned aerial vehicles (UAVs), we publicly release all our code, datasets, and trained networks.

Includes members:
  Member "cmsis nn efficient neural network kernels for arm cortex m cpus":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rnnpool efficient non linear pooling for ram constrained inference")
  Member "an iot endpoint system on chip for secure and energy efficient near sensor analytics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pulp nn accelerating quantized neural networks on parallel ultra low power risc v processors")
  Member "learning to fly by crashing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep drone racing from simulation to reality with domain randomization")
  Member "efficient optical flow and stereo vision for velocity estimation and obstacle avoidance on an autonomous pocket drone":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hundred drones land in a minute")
  Member "cad2rl real single image flight without a single real image":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "increasing generality in machine learning through procedural content generation")
  Member "local histogram matching for efficient optical flow computation applied to velocity estimation on pocket drones":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an open source and open hardware deep learning powered visual navigation engine for autonomous nano uavs")
  Member "quantized neural networks training neural networks with low precision weights and activations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "switchable precision neural networks")

Question: Please tell me which class the source paper 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)