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: "gapflyt active vision based minimalist structure less gap detection for quadrotor flight"
  Abstract: Although quadrotors, and aerial robots in general, are inherently active agents, their perceptual capabilities in literature so far have been mostly passive in nature. Researchers and practitioners today use traditional computer vision algorithms with the aim of building a representation of general applicability: a 3D reconstruction of the scene. Using this representation, planning tasks are constructed and accomplished to allow the quadrotor to demonstrate autonomous behavior. These methods are inefficient as they are not task driven and such methodologies are not utilized by flying insects and birds. Such agents have been solving the problem of navigation and complex control for ages without the need to build a 3D map and are highly task driven. In this paper, we propose this framework of bio-inspired perceptual design for quadrotors. We use this philosophy to design a minimalist sensori-motor framework for a quadrotor to fly though unknown gaps without a 3D reconstruction of the scene using only a monocular camera and onboard sensing. We successfully evaluate and demonstrate the proposed approach in many real-world experiments with different settings and window shapes, achieving a success rate of 85% at 2.5ms$^{-1}$ even with a minimum tolerance of just 5cm. To our knowledge, this is the first paper which addresses the problem of gap detection of an unknown shape and location with a monocular camera and onboard sensing.

Connected through hyperedge HE-1 (induced by source paper "learning to seek deep reinforcement learning for phototaxis of a nano drone in an obstacle field"):
  with members:
    - "air learning an ai research platform for algorithm hardware benchmarking of autonomous aerial robots"
    - "low level control of a quadrotor with deep model based reinforcement learning"
    - "a 64 mw dnn based visual navigation engine for autonomous nano drones"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "sim to multi real transfer of low level robust control policies to multiple quadrotors"
    - "fly safe aerial swarm robotics using force field particle swarm optimisation"
    - "evolving rewards to automate reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"

Connected through hyperedge HE-2 (induced by source paper "visual model predictive localization for computationally efficient autonomous racing of a 72 gram drone"):
  with members:
    - "deep drone racing learning agile flight in dynamic environments"
    - "autonomous drone race a computationally efficient vision based navigation and control strategy"
    - "beauty and the beast optimal methods meet learning for drone racing"
    - "aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision"
    - "efficient optical flow and stereo vision for velocity estimation and obstacle avoidance on an autonomous pocket drone"

Connected through hyperedge HE-3 (induced by source paper "online deep learning for improved trajectory tracking of unmanned aerial vehicles using expert knowledge"):
  with members:
    - "an inversion based learning approach for improving impromptu trajectory tracking of robots with non minimum phase dynamics"
    - "design of deep neural networks as add on blocks for improving impromptu trajectory tracking"
    - "learning quadrotor dynamics using neural network for flight control"
    - "deep neural networks for improved impromptu trajectory tracking of quadrotors"

Connected through hyperedge HE-4 (induced by source paper "end to end collision avoidance based on optical flow and neural networks"):
  with members:
    - "efficient optical flow and stereo vision for velocity estimation and obstacle avoidance on an autonomous pocket drone"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "salientdso bringing attention to direct sparse odometry"):
  with members:
    - "interactive perception leveraging action in perception and perception in action"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "direct sparse odometry"
    - "pyramid scene parsing network"
    - "a photometrically calibrated benchmark for monocular visual odometry"
    - "revisiting active perception"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-6 (induced by source paper "autonomous drone race a computationally efficient vision based navigation and control strategy"):
  with members:
    - "deep drone racing learning agile flight in dynamic environments"
    - "oil observational imitation learning"
    - "aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision"
    - "faster r cnn towards real time object detection with region proposal networks"

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)