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: "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.

Connected through hyperedge HE-1 (induced by source paper "learning depth via interaction"):
  with members:
    - "towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "deeptam deep tracking and mapping"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "unsupervised learning of shape and pose with differentiable point clouds"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"

Connected through hyperedge HE-2 (induced by source paper "pulp nn accelerating quantized neural networks on parallel ultra low power risc v processors"):
  with members:
    - "memory driven mixed low precision quantization for enabling deep network inference on microcontrollers"
    - "xnor neural engine a hardware accelerator ip for 21 6 fj op binary neural network inference"
    - "haq hardware aware automated quantization with mixed precision"
    - "neura ghe exploiting cpu fpga synergies for efficient and flexible cnn inference acceleration on zynq socs"
    - "pact parameterized clipping activation for quantized neural networks"
    - "cmsis nn efficient neural network kernels for arm cortex m cpus"
    - "minimum energy quantized neural networks"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"

Connected through hyperedge HE-3 (induced by source paper "learning to seek deep reinforcement learning for phototaxis of a nano drone in an obstacle field"):
  with members:
    - "air learning an ai research platform for algorithm hardware benchmarking of autonomous aerial robots"
    - "low level control of a quadrotor with deep model based reinforcement learning"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "sim to multi real transfer of low level robust control policies to multiple quadrotors"
    - "fly safe aerial swarm robotics using force field particle swarm optimisation"
    - "evolving rewards to automate reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning for uav attitude control"

Connected through hyperedge HE-4 (induced by source paper "ebpc extended bit plane compression for deep neural network inference and training accelerators"):
  with members:
    - "and the bit goes down revisiting the quantization of neural networks"
    - "additive noise annealing and approximation properties of quantized neural networks"
    - "rpr random partition relaxation for training binary and ternary weight neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "multiposenet fast multi person pose estimation using pose residual network"
    - "dnn feature map compression using learned representation over gf 2"

Connected through hyperedge HE-5 (induced by source paper "additive noise annealing and approximation properties of quantized neural networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "yolov3 an incremental improvement"
    - "extended bit plane compression for convolutional neural network accelerators"
    - "training compact neural networks with binary weights and low precision activations"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "towards accurate binary convolutional neural network"
    - "learning to fly by crashing"

Connected through hyperedge HE-6 (induced by source paper "a general framework for uncertainty estimation in deep learning"):
  with members:
    - "where is the information in a deep neural network"
    - "lightweight probabilistic deep networks"
    - "deep drone racing learning agile flight in dynamic environments"
    - "the limits and potentials of deep learning for robotics"
    - "ensemble bayesian decision making with redundant deep perceptual control policies"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "beauty and the beast optimal methods meet learning for drone racing"

Connected through hyperedge HE-7 (induced by source paper "fann on mcu an open source toolkit for energy efficient neural network inference at the edge of the internet of things"):
  with members:
    - "deep learning architect classification for architectural design through the eye of artificial intelligence"
    - "cbinfer exploiting frame to frame locality for faster convolutional network inference on video streams"
    - "cmsis nn efficient neural network kernels for arm cortex m cpus"
    - "towards interpretable deep neural networks by leveraging adversarial examples"
    - "origami a 803 gop s w convolutional network accelerator"
    - "cbinfer change based inference for convolutional neural networks on video data"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "an analysis of visitors behavior in the louvre museum a study using bluetooth data"

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)