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: "how hard is it to cross the room training recurrent neural networks to steer a uav"
  Abstract: This work explores the feasibility of steering a drone with a (recurrent) neural network, based on input from a forward looking camera, in the context of a high-level navigation task. We set up a generic framework for training a network to perform navigation tasks based on imitation learning. It can be applied to both aerial and land vehicles. As a proof of concept we apply it to a UAV (Unmanned Aerial Vehicle) in a simulated environment, learning to cross a room containing a number of obstacles. So far only feedforward neural networks (FNNs) have been used to train UAV control. To cope with more complex tasks, we propose the use of recurrent neural networks (RNN) instead and successfully train an LSTM (Long-Short Term Memory) network for controlling UAVs. Vision based control is a sequential prediction problem, known for its highly correlated input data. The correlation makes training a network hard, especially an RNN. To overcome this issue, we investigate an alternative sampling method during training, namely window-wise truncated backpropagation through time (WW-TBPTT). Further, end-to-end training requires a lot of data which often is not available. Therefore, we compare the performance of retraining only the Fully Connected (FC) and LSTM control layers with networks which are trained end-to-end. Performing the relatively simple task of crossing a room already reveals important guidelines and good practices for training neural control networks. Different visualizations help to explain the behavior learned.

Connected through hyperedge HE-1 (induced by source paper "deep convolutional neural network based autonomous drone navigation"):
  with members:
    - "active neural localization"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "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"
    - "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-2 (induced by source paper "drone path following in gps denied environments using convolutional networks"):
  with members:
    - "active neural localization"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "relative camera pose estimation using convolutional neural networks"
    - "deep neural network for real time autonomous indoor navigation"
    - "fast r cnn"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"

Connected through hyperedge HE-3 (induced by source paper "design challenges of multi uav systems in cyber physical applications a comprehensive survey and future directions"):
  with members:
    - "unmanned aerial vehicles uavs a survey on civil applications and key research challenges"
    - "oil observational imitation learning"
    - "uplink coverage performance of an underlay drone cell for temporary events"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "ultra low power deep learning powered autonomous nano drones"
    - "a survey of machine and deep learning methods for internet of things iot security"
    - "prospect theory for enhanced cyber physical security of drone delivery systems a network interdiction game"
    - "the blockchain a new framework for robotic swarm systems"

Connected through hyperedge HE-4 (induced by source paper "learning to navigate autonomously in outdoor environments mavnet"):
  with members:
    - "learning to fly by crashing"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deep neural network for real time autonomous indoor navigation"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "learning monocular reactive uav control in cluttered natural environments"

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.CV(Computer Vision and Pattern Recognition)