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: "pushbroom stereo for high speed navigation in cluttered environments"
  Abstract: We present a novel stereo vision algorithm that is capable of obstacle detection on a mobile-CPU processor at 120 frames per second. Our system performs a subset of standard block-matching stereo processing, searching only for obstacles at a single depth. By using an onboard IMU and state-estimator, we can recover the position of obstacles at all other depths, building and updating a full depth-map at framerate. #R##N#Here, we describe both the algorithm and our implementation on a high-speed, small UAV, flying at over 20 MPH (9 m/s) close to obstacles. The system requires no external sensing or computation and is, to the best of our knowledge, the first high-framerate stereo detection system running onboard a small UAV.

Connected through hyperedge HE-1 (induced by source paper "flexible trinocular non rigid multi camera imu dense reconstruction for uav navigation and mapping"):
  with members:
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "robust stereo visual inertial odometry for fast autonomous flight"
    - "flexible stereo constrained non rigid wide baseline stereo vision for fixed wing aerial platforms"

Connected through hyperedge HE-2 (induced by source paper "online path generation and navigation for swarms of uavs"):
  with members:
    - "towards the verification of safety critical autonomous systems in dynamic environments"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-3 (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"
    - "robust monocular flight in cluttered outdoor environments"
    - "you only look once unified real time object detection"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "learning unmanned aerial vehicle control for autonomous target following"):
  with members:
    - "gesture based piloting of an aerial robot using monocular vision"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "benchmarking deep reinforcement learning for continuous control"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"

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"
    - "vision and learning for deliberative monocular cluttered flight"

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"
    - "vision and learning for deliberative monocular cluttered flight"
    - "learning monocular reactive uav control in cluttered natural environments"

Connected through hyperedge HE-7 (induced by source paper "cad2rl real single image flight without a single real image"):
  with members:
    - "cognitive mapping and planning for visual navigation"
    - "end to end learning for self driving cars"
    - "decentralized non communicating multiagent collision avoidance with deep reinforcement learning"
    - "playing for data ground truth from computer games"
    - "sim to real robot learning from pixels with progressive nets"
    - "deep reinforcement learning with successor features for navigation across similar environments"
    - "simultaneous deep transfer across domains and tasks"
    - "deep neural network for real time autonomous indoor navigation"

Connected through hyperedge HE-8 (induced by source paper "high performance and tunable stereo reconstruction"):
  with members:
    - "computing the stereo matching cost with a convolutional neural network"
    - "monocular slam supported object recognition"

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)