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: "the blackbird dataset a large scale dataset for uav perception in aggressive flight"
  Abstract: The Blackbird unmanned aerial vehicle (UAV) dataset is a large-scale, aggressive indoor flight dataset collected using a custom-built quadrotor platform for use in evaluation of agile perception.Inspired by the potential of future high-speed fully-autonomous drone racing, the Blackbird dataset contains over 10 hours of flight data from 168 flights over 17 flight trajectories and 5 environments at velocities up to $7.0ms^-1$. Each flight includes sensor data from 120Hz stereo and downward-facing photorealistic virtual cameras, 100Hz IMU, $\sim190Hz$ motor speed sensors, and 360Hz millimeter-accurate motion capture ground truth. Camera images for each flight were photorealistically rendered using FlightGoggles across a variety of environments to facilitate easy experimentation of high performance perception algorithms. The dataset is available for download at this http URL

Connected through hyperedge HE-1 (induced by source paper "airsim drone racing lab"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "flying through a narrow gap using neural network an end to end planning and control approach"
    - "event based vision a survey"
    - "flightgoggles photorealistic sensor simulation for perception driven robotics using photogrammetry and virtual reality"
    - "fiesta fast incremental euclidean distance fields for online motion planning of aerial robots"
    - "deep drone racing learning agile flight in dynamic environments"
    - "a real time game theoretic planner for autonomous two player drone racing"
    - "proflow learning to predict optical flow"

Connected through hyperedge HE-2 (induced by source paper "au air a multi modal unmanned aerial vehicle dataset for low altitude traffic surveillance"):
  with members:
    - "autonomous aerial cinematography in unstructured environments with learned artistic decision making"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "vision meets drones a challenge"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "the unmanned aerial vehicle benchmark object detection and tracking"
    - "the highd dataset a drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems"

Connected through hyperedge HE-3 (induced by source paper "closed loop benchmarking of stereo visual inertial slam systems understanding the impact of drift and latency on tracking accuracy"):
  with members:
    - "good feature matching towards accurate robust vo vslam with low latency"
    - "a general optimization based framework for local odometry estimation with multiple sensors"
    - "kimera an open source library for real time metric semantic localization and mapping"
    - "characterizing slam benchmarks and methods for the robust perception age"
    - "navigating the landscape for real time localization and mapping for robotics and virtual and augmented reality"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "a complete system for vision based micro aerial vehicle mapping planning and flight in cluttered environments"
    - "robust stereo visual inertial odometry for fast autonomous flight"

Connected through hyperedge HE-4 (induced by source paper "characterizing slam benchmarks and methods for the robust perception age"):
  with members:
    - "good feature matching towards accurate robust vo vslam with low latency"
    - "low latency visual slam with appearance enhanced local map building"
    - "good feature selection for least squares pose optimization in vo vslam"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "the tum vi benchmark for evaluating visual inertial odometry"
    - "direct sparse odometry"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "a photometrically calibrated benchmark for monocular visual odometry"

Connected through hyperedge HE-5 (induced by source paper "flightgoggles photorealistic sensor simulation for perception driven robotics using photogrammetry and virtual reality"):
  with members:
    - "sim to real learning agile locomotion for quadruped robots"
    - "accurate tracking of aggressive quadrotor trajectories using incremental nonlinear dynamic inversion and differential flatness"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "on manifold preintegration theory for fast and accurate visual inertial navigation"

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)