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: "flightgoggles photorealistic sensor simulation for perception driven robotics using photogrammetry and virtual reality"
  Abstract: FlightGoggles is a photorealistic sensor simulator for perception-driven robotic vehicles. The key contributions of FlightGoggles are twofold. First, FlightGoggles provides photorealistic exteroceptive sensor simulation using graphics assets generated with photogrammetry. Second, it also provides the ability to combine $\textit{(i)}$ synthetic exteroceptive measurements generated $\textit{in silico}$ in real time and $\textit{(ii)}$ vehicle dynamics and proprioceptive measurements generated $\textit{in motio}$ by vehicle(s) in flight in a motion-capture facility. FlightGoggles is capable of simulating a virtual-reality environment around autonomous vehicle(s) in flight. While a vehicle is in flight in the FlightGoggles virtual reality environment, exteroceptive sensors are rendered synthetically in real time while all complex extrinsic dynamics are generated organically through the natural interactions of the vehicle. The FlightGoggles framework allows for researchers to accelerate development by circumventing the need to estimate complex and hard-to-model interactions such as aerodynamics, motor mechanics, battery electrochemistry, and behavior of other agents. The ability to perform vehicle-in-the-loop experiments with photorealistic exteroceptive sensor simulation facilitates novel research directions involving, $\textit{e.g.}$, fast and agile autonomous flight in obstacle-rich environments, safe human interaction, and flexible sensor selection. FlightGoggles has been utilized as the main test for selecting nine teams that will advance in the AlphaPilot autonomous drone racing challenge. Subsequently, FlightGoggles has been actively used by the community. We survey approaches and results from the top twenty AlphaPilot teams, which may be of independent interest.

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"
    - "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"
    - "the blackbird dataset a large scale dataset for uav perception in aggressive flight"
    - "proflow learning to predict optical flow"

Connected through hyperedge HE-2 (induced by source paper "aggressive perception aware navigation using deep optical flow dynamics and pixelmpc"):
  with members:
    - "deep drone racing learning agile flight in dynamic environments"
    - "yolov3 an incremental improvement"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "pampc perception aware model predictive control for quadrotors"
    - "beauty and the beast optimal methods meet learning for drone racing"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "agile autonomous driving using end to end deep imitation learning"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"

Connected through hyperedge HE-3 (induced by source paper "cinemairsim a camera realistic robotics simulator for cinematographic purposes"):
  with members:
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "unrealcv connecting computer vision to unreal engine"
    - "automated cinematography with unmanned aerial vehicles"
    - "airways optimization based planning of quadrotor trajectories according to high level user goals"
    - "generic drone control platform for autonomous capture of cinema scenes"
    - "webotstm professional mobile robot simulation"

Connected through hyperedge HE-4 (induced by source paper "virtual reality for robots"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "toward a language theoretic foundation for planning and filtering"
    - "strategies to design signals to spoof kalman filter"
    - "functional forms of optimum spoofing attacks for vector parameter estimation in quantized sensor networks"

Connected through hyperedge HE-5 (induced by source paper "the openuav swarm simulation testbed a collaborative designstudio for field robotics"):
  with members:
    - "geomorphological analysis using unpiloted aircraft systems structure from motion and deep learning"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "learning from the hindsight plan episodic mpc improvement"

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)