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: "learning transferable uav for forest visual perception"
  Abstract: In this paper, we propose a new pipeline of training a monocular UAV to fly a collision-free trajectory along the dense forest trail. As gathering high-precision images in the real world is expensive and the off-the-shelf dataset has some deficiencies, we collect a new dense forest trail dataset in a variety of simulated environment in Unreal Engine. Then we formulate visual perception of forests as a classification problem. A ResNet-18 model is trained to decide the moving direction frame by frame. To transfer the learned strategy to the real world, we construct a ResNet-18 adaptation model via multi-kernel maximum mean discrepancies to leverage the relevant labelled data and alleviate the discrepancy between simulated and real environment. Simulation and real-world flight with a variety of appearance and environment changes are both tested. The ResNet-18 adaptation and its variant model achieve the best result of 84.08% accuracy in reality.

Connected through hyperedge HE-1 (induced by source paper "improved reinforcement learning through imitation learning pretraining towards image based autonomous driving"):
  with members:
    - "learning accurate comfortable and human like driving"
    - "drone path following in gps denied environments using convolutional networks"
    - "bam bottleneck attention module"
    - "cbam convolutional block attention module"
    - "towards monocular vision based obstacle avoidance through deep reinforcement learning"
    - "squeeze and excitation networks"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "cad2rl real single image flight without a single real image"

Connected through hyperedge HE-2 (induced by source paper "deep learning based wearable assistive system for visually impaired people"):
  with members:
    - "deepvio self supervised deep learning of monocular visual inertial odometry using 3d geometric constraints"
    - "wearable travel aid for environment perception and navigation of visually impaired people"
    - "a unified framework for mutual improvement of slam and semantic segmentation"
    - "deep global relative networks for end to end 6 dof visual localization and odometry"
    - "rednet residual encoder decoder network for indoor rgb d semantic segmentation"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "smart guiding glasses for visually impaired people in indoor environment"
    - "the cityscapes dataset for semantic urban scene understanding"

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)