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: "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"
  Abstract: Event cameras are bioinspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. These cameras do not suffer from motion blur and have a very high dynamic range, which enables them to provide reliable visual information during high-speed motions or in scenes characterized by high dynamic range. However, event cameras output only little information when the amount of motion is limited, such as in the case of almost still motion. Conversely, standard cameras provide instant and rich information about the environment most of the time (in low-speed and good lighting scenarios), but they fail severely in case of fast motions, or difficult lighting such as high dynamic range or low light scenes. In this letter, we present the first state estimation pipeline that leverages the complementary advantages of these two sensors by fusing in a tightly coupled manner events, standard frames, and inertial measurements. We show on the publicly available Event Camera Dataset that our hybrid pipeline leads to an accuracy improvement of 130% over event-only pipelines, and 85% over standard-frames-only visual-inertial systems, while still being computationally tractable. Furthermore, we use our pipeline to demonstrate-to the best of our knowledge-the first autonomous quadrotor flight using an event camera for state estimation, unlocking flight scenarios that were not reachable with traditional visual-inertial odometry, such as low-light environments and high dynamic range scenes. Videos of the experiments: http://rpg.ifi.uzh.ch/ultimateslam.html.

Connected through hyperedge HE-1 (induced by source paper "deep drone racing from simulation to reality with domain randomization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep drone racing learning agile flight in dynamic environments"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "autonomous drone race a computationally efficient vision based navigation and control strategy"
    - "beauty and the beast optimal methods meet learning for drone racing"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "self supervised deep reinforcement learning with generalized computation graphs for robot navigation"
    - "teaching uavs to race end to end regression of agile controls in simulation"

Connected through hyperedge HE-2 (induced by source paper "exploring adversarial attack in spiking neural networks with spike compatible gradient"):
  with members:
    - "a comprehensive analysis on adversarial robustness of spiking neural networks"
    - "snn under attack are spiking deep belief networks vulnerable to adversarial examples"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "adversarial patch"
    - "direct training for spiking neural networks faster larger better"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-3 (induced by source paper "globally optimal contrast maximisation for event based motion estimation"):
  with members:
    - "event based motion segmentation by motion compensation"
    - "event based star tracking via multiresolution progressive hough transforms"
    - "focus is all you need loss functions for event based vision"
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "unsupervised event based learning of optical flow depth and egomotion"
    - "star tracking using an event camera"
    - "eventnet asynchronous recursive event processing"
    - "ev flownet self supervised optical flow estimation for event based cameras"

Connected through hyperedge HE-4 (induced by source paper "redesigning slam for arbitrary multi camera systems"):
  with members:
    - "voxel map for visual slam"
    - "project autovision localization and 3d scene perception for an autonomous vehicle with a multi camera system"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "direct sparse odometry"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-5 (induced by source paper "long duration fully autonomous operation of rotorcraft unmanned aerial systems for remote sensing data acquisition"):
  with members:
    - "long duration autonomy for small rotorcraft uas including recharging"
    - "autonomous landing of a multirotor micro air vehicle on a high velocity ground vehicle"

Connected through hyperedge HE-6 (induced by source paper "event based vision a survey"):
  with members:
    - "event based motion segmentation by motion compensation"
    - "ev imo motion segmentation dataset and learning pipeline for event cameras"
    - "atis spinnaker a fully event based visual tracking demonstration"
    - "ced color event camera dataset"
    - "unsupervised learning of a hierarchical spiking neural network for optical flow estimation from events to global motion perception"
    - "high speed and high dynamic range video with an event camera"
    - "end to end learning of representations for asynchronous event based data"
    - "speed invariant time surface for learning to detect corner points with event based cameras"

Connected through hyperedge HE-7 (induced by source paper "high speed and high dynamic range video with an event camera"):
  with members:
    - "events to video bringing modern computer vision to event cameras"
    - "ced color event camera dataset"
    - "event based vision a survey"
    - "semi dense 3d reconstruction with a stereo event camera"
    - "yolov3 an incremental improvement"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"
    - "learning blind video temporal consistency"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-8 (induced by source paper "video to events bringing modern computer vision closer to event cameras"):
  with members:
    - "events to video bringing modern computer vision to event cameras"
    - "graph based object classification for neuromorphic vision sensing"
    - "high speed and high dynamic range video with an event camera"
    - "end to end learning of representations for asynchronous event based data"
    - "event based vision a survey"
    - "unsupervised event based learning of optical flow depth and egomotion"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"

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)