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: "vision and learning for deliberative monocular cluttered flight"
  Abstract: Cameras provide a rich source of information while being passive, cheap and lightweight for small and medium Unmanned Aerial Vehicles (UAVs). In this work we present the first implementation of receding horizon control, which is widely used in ground vehicles, with monocular vision as the only sensing mode for autonomous UAV flight in dense clutter. We make it feasible on UAVs via a number of contributions: novel coupling of perception and control via relevant and diverse, multiple interpretations of the scene around the robot, leveraging recent advances in machine learning to showcase anytime budgeted cost-sensitive feature selection, and fast non-linear regression for monocular depth prediction. We empirically demonstrate the efficacy of our novel pipeline via real world experiments of more than 2 kms through dense trees with a quadrotor built from off-the-shelf parts. Moreover our pipeline is designed to combine information from other modalities like stereo and lidar as well if available.

Connected through hyperedge HE-1 (induced by source paper "multi task regression based learning for autonomous unmanned aerial vehicle flight control within unstructured outdoor environments"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "veritatem dies aperit temporally consistent depth prediction enabled by a multi task geometric and semantic scene understanding approach"
    - "deep drone racing learning agile flight in dynamic environments"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "fast autonomous flight in gps denied and cluttered environments"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "unsupervised learning of depth and ego motion from video"
    - "aggressive deep driving model predictive control with a cnn cost model"

Connected through hyperedge HE-2 (induced by source paper "fast and agile vision based flight with teleoperation and collision avoidance on a multirotor"):
  with members:
    - "nanomap fast uncertainty aware proximity queries with lazy search over local 3d data"
    - "vins mono a robust and versatile monocular visual inertial state estimator"

Connected through hyperedge HE-3 (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"
    - "robust monocular flight in cluttered outdoor environments"

Connected through hyperedge HE-4 (induced by source paper "real time planning with multi fidelity models for agile flights in unknown environments"):
  with members:
    - "nanomap fast uncertainty aware proximity queries with lazy search over local 3d data"
    - "safe local exploration for replanning in cluttered unknown environments for microaerial vehicles"
    - "search based motion planning for aggressive flight in se 3"
    - "search based motion planning for quadrotors using linear quadratic minimum time control"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"

Connected through hyperedge HE-5 (induced by source paper "fast accurate thin structure obstacle detection for autonomous mobile robots"):
  with members:
    - "fast robust monocular depth estimation for obstacle detection with fully convolutional networks"
    - "lost and found detecting small road hazards for self driving vehicles"
    - "detecting unexpected obstacles for self driving cars fusing deep learning and geometric modeling"
    - "direct sparse odometry"
    - "pushbroom stereo for high speed navigation in cluttered environments"

Connected through hyperedge HE-6 (induced by source paper "obstacle avoidance strategy using onboard stereo vision on a flapping wing mav"):
  with members:
    - "orb slam a versatile and accurate monocular slam system"
    - "pushbroom stereo for high speed navigation in cluttered environments"
    - "learning monocular reactive uav control in cluttered natural environments"

Connected through hyperedge HE-7 (induced by source paper "fastrack a modular framework for fast and guaranteed safe motion planning"):
  with members:
    - "funnel libraries for real time robust feedback motion planning"
    - "safe sequential path planning of multi vehicle systems under presence of disturbances and imperfect information"

Connected through hyperedge HE-8 (induced by source paper "introspective perception learning to predict failures in vision systems"):
  with members:
    - "robust monocular flight in cluttered outdoor environments"
    - "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"
    - "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"

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)