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: "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
  Abstract: We present a micro aerial vehicle (MAV) system, built with inexpensive off-the-shelf hardware, for autonomously following trails in unstructured, outdoor environments such as forests. The system introduces a deep neural network (DNN) called TrailNet for estimating the view orientation and lateral offset of the MAV with respect to the trail center. The DNN-based controller achieves stable flight without oscillations by avoiding overconfident behavior through a loss function that includes both label smoothing and entropy reward. In addition to the TrailNet DNN, the system also utilizes vision modules for environmental awareness, including another DNN for object detection and a visual odometry component for estimating depth for the purpose of low-level obstacle detection. All vision systems run in real time on board the MAV via a Jetson TX1. We provide details on the hardware and software used, as well as implementation details. We present experiments showing the ability of our system to navigate forest trails more robustly than previous techniques, including autonomous flights of 1 km.

Connected through hyperedge HE-1 (induced by source paper "aggressive perception aware navigation using deep optical flow dynamics and pixelmpc"):
  with members:
    - "flightgoggles photorealistic sensor simulation for perception driven robotics using photogrammetry and virtual reality"
    - "deep drone racing learning agile flight in dynamic environments"
    - "yolov3 an incremental improvement"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "pampc perception aware model predictive control for quadrotors"
    - "beauty and the beast optimal methods meet learning for drone racing"
    - "agile autonomous driving using end to end deep imitation learning"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning based visual navigation with information theoretic regularization"):
  with members:
    - "learning a controller fusion network by online trajectory filtering for vision based uav racing"
    - "sim real joint reinforcement transfer for 3d indoor navigation"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "bayesian relational memory for semantic visual navigation"
    - "reinforcement learning from imperfect demonstrations"
    - "deep drone racing learning agile flight in dynamic environments"
    - "a real time game theoretic planner for autonomous two player drone racing"
    - "oil observational imitation learning"

Connected through hyperedge HE-3 (induced by source paper "terminal brain damage exposing the graceless degradation in deep neural networks under hardware fault attacks"):
  with members:
    - "page cache attacks"
    - "scalable methods for 8 bit training of neural networks"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "sever a robust meta algorithm for stochastic optimization"
    - "breaking transferability of adversarial samples with randomness"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "training deep neural networks with 8 bit floating point numbers"
    - "nethammer inducing rowhammer faults through network requests"

Connected through hyperedge HE-4 (induced by source paper "deep convolutional neural network based autonomous drone navigation"):
  with members:
    - "active neural localization"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "relative camera pose estimation using convolutional neural networks"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "how hard is it to cross the room training recurrent neural networks to steer a uav"
    - "end to end learning for self driving cars"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "what happens if learning to predict the effect of forces in images"

Connected through hyperedge HE-5 (induced by source paper "learning to navigate from simulation via spatial and semantic information synthesis"):
  with members:
    - "yolov3 an incremental improvement"
    - "conditional affordance learning for driving in urban environments"
    - "virtual to real learning to control in visual semantic segmentation"
    - "navigation by imitation in a pedestrian rich environment"
    - "driving policy transfer via modularity and abstraction"
    - "a data efficient framework for training and sim to real transfer of navigation policies"
    - "visual representations for semantic target driven navigation"
    - "virtual to real reinforcement learning for autonomous driving"

Connected through hyperedge HE-6 (induced by source paper "physical adversarial textures that fool visual object tracking"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "a general framework for adversarial examples with objectives"
    - "deepbillboard systematic physical world testing of autonomous driving systems"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"
    - "end to end representation learning for correlation filter based tracking"
    - "underwater multi robot convoying using visual tracking by detection"
    - "detect to track and track to detect"
    - "synthesizing robust adversarial examples"

Connected through hyperedge HE-7 (induced by source paper "learning to navigate from simulation via spatial and semantic information synthesis with noise model embedding"):
  with members:
    - "yolov3 an incremental improvement"
    - "conditional affordance learning for driving in urban environments"
    - "navigation by imitation in a pedestrian rich environment"
    - "a data efficient framework for training and sim to real transfer of navigation policies"
    - "virtual to real reinforcement learning for autonomous driving"
    - "unsupervised learning of depth and ego motion from video"
    - "learning to fly by crashing"
    - "socially aware motion planning with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "high throughput cnn inference on embedded arm big little multi core processors"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "tvm end to end optimization stack for deep learning"
    - "ai benchmark running deep neural networks on android smartphones"
    - "mobile machine learning hardware at arm a systems on chip soc perspective"
    - "scheduling computation graphs of deep learning models on manycore cpus"
    - "optimizing cnn model inference on cpus"
    - "modeling the resource requirements of convolutional neural networks on mobile devices"

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)