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: "robust monocular flight in cluttered outdoor environments"
  Abstract: Recently, there have been numerous advances in the development of biologically inspired lightweight Micro Aerial Vehicles (MAVs). While autonomous navigation is fairly straight-forward for large UAVs as expensive sensors and monitoring devices can be employed, robust methods for obstacle avoidance remains a challenging task for MAVs which operate at low altitude in cluttered unstructured environments. Due to payload and power constraints, it is necessary for such systems to have autonomous navigation and flight capabilities using mostly passive sensors such as cameras. In this paper, we describe a robust system that enables autonomous navigation of small agile quad-rotors at low altitude through natural forest environments. We present a direct depth estimation approach that is capable of producing accurate, semi-dense depth-maps in real time. Furthermore, a novel wind-resistant control scheme is presented that enables stable way-point tracking even in the presence of strong winds. We demonstrate the performance of our system through extensive experiments on real images and field tests in a cluttered outdoor environment.

Connected through hyperedge HE-1 (induced by source paper "gonet a semi supervised deep learning approach for traversability estimation"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "learning to fly by crashing"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "to go or not to go a near unsupervised learning approach for robot navigation"
    - "nips 2016 tutorial generative adversarial networks"
    - "semantic image inpainting with deep generative models"
    - "generative visual manipulation on the natural image manifold"

Connected through hyperedge HE-2 (induced by source paper "j mod 2 joint monocular obstacle detection and depth estimation"):
  with members:
    - "analyzing modular cnn architectures for joint depth prediction and semantic segmentation"
    - "obstacle avoidance through deep networks based intermediate perception"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "3d bounding box estimation using deep learning and geometry"
    - "you only look once unified real time object detection"
    - "very deep convolutional networks for large scale image recognition"
    - "depth map prediction from a single image using a multi scale deep network"

Connected through hyperedge HE-3 (induced by source paper "learning transferable uav for forest visual perception"):
  with members:
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "learning transferable policies for monocular reactive mav control"
    - "deep transfer learning with joint adaptation networks"
    - "learning transferable features with deep adaptation networks"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "end to end training of deep visuomotor policies"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-4 (induced by source paper "nanomap fast uncertainty aware proximity queries with lazy search over local 3d data"):
  with members:
    - "real time trajectory replanning for mavs using uniform b splines and a 3d circular buffer"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"
    - "learning monocular reactive uav control in cluttered natural environments"

Connected through hyperedge HE-5 (induced by source paper "how hard is it to cross the room training recurrent neural networks to steer a uav"):
  with members:
    - "cad2rl real single image flight without a single real image"
    - "end to end learning for self driving cars"
    - "aggressive quadrotor flight through narrow gaps with onboard sensing and computing"
    - "flownet learning optical flow with convolutional networks"
    - "adam a method for stochastic optimization"
    - "depth map prediction from a single image using a multi scale deep network"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-6 (induced by source paper "introspective perception learning to predict failures in vision systems"):
  with members:
    - "metareasoning for planning under uncertainty"
    - "caffe convolutional architecture for fast feature embedding"
    - "depth map prediction from a single image using a multi scale deep network"
    - "vision and learning for deliberative monocular cluttered flight"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "two stream convolutional networks for action recognition in videos"
    - "deep learning using linear support vector machines"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

Connected through hyperedge HE-7 (induced by source paper "learning transferable policies for monocular reactive mav control"):
  with members:
    - "building with drones accurate 3d facade reconstruction using mavs"
    - "learning transferable features with deep adaptation networks"
    - "end to end training of deep visuomotor policies"
    - "how transferable are features in deep neural networks"
    - "domain adaptive neural networks for object recognition"
    - "vision and learning for deliberative monocular cluttered flight"
    - "decaf a deep convolutional activation feature for generic visual 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.RO(Robotics)