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: "deep neural network for real time autonomous indoor navigation"
  Abstract: Autonomous indoor navigation of Micro Aerial Vehicles (MAVs) possesses many challenges. One main reason is that GPS has limited precision in indoor environments. The additional fact that MAVs are not able to carry heavy weight or power consuming sensors, such as range finders, makes indoor autonomous navigation a challenging task. In this paper, we propose a practical system in which a quadcopter autonomously navigates indoors and finds a specific target, i.e., a book bag, by using a single camera. A deep learning model, Convolutional Neural Network (ConvNet), is used to learn a controller strategy that mimics an expert pilot's choice of action. We show our system's performance through real-time experiments in diverse indoor locations. To understand more about our trained network, we use several visualization techniques.

Connected through hyperedge HE-1 (induced by source paper "multiplicative controller fusion a hybrid navigation strategy for deployment in unknown environments"):
  with members:
    - "policies modulating trajectory generators"
    - "residual reactive navigation combining classical and learned navigation strategies for deployment in unknown environments"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "on evaluation of embodied navigation agents"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "learning with training wheels speeding up training with a simple controller for deep reinforcement learning"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"

Connected through hyperedge HE-2 (induced by source paper "combining optimal control and learning for visual navigation in novel environments"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "neural autonomous navigation with riemannian motion policy"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "deep drone racing learning agile flight in dynamic environments"
    - "learning navigation behaviors end to end with autorl"
    - "semi parametric topological memory for navigation"
    - "variational end to end navigation and localization"

Connected through hyperedge HE-3 (induced by source paper "deep convolutional neural network based autonomous drone navigation"):
  with members:
    - "active neural localization"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "relative camera pose estimation using convolutional neural networks"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "how hard is it to cross the room training recurrent neural networks to steer a uav"
    - "end to end learning for self driving cars"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "predicting unobserved space for planning via depth map augmentation"):
  with members:
    - "depth coefficients for depth completion"
    - "fusion of stereo and still monocular depth estimates in a self supervised learning context"
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "deep depth completion of a single rgb d image"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "safe local exploration for replanning in cluttered unknown environments for microaerial vehicles"

Connected through hyperedge HE-5 (induced by source paper "residual reactive navigation combining classical and learned navigation strategies for deployment in unknown environments"):
  with members:
    - "estimating risk and uncertainty in deep reinforcement learning"
    - "policies modulating trajectory generators"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "residual policy learning"
    - "on evaluation of embodied navigation agents"
    - "residual reinforcement learning for robot control"
    - "addressing function approximation error in actor critic methods"
    - "learning with training wheels speeding up training with a simple controller for deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "generating robust supervision for learning based visual navigation using hamilton jacobi reachability"):
  with members:
    - "an efficient reachability based framework for provably safe autonomous navigation in unknown environments"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "neural autonomous navigation with riemannian motion policy"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "deep drone racing learning agile flight in dynamic environments"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "driving policy transfer via modularity and abstraction"
    - "beauty and the beast optimal methods meet learning for drone racing"

Connected through hyperedge HE-7 (induced by source paper "learning a controller fusion network by online trajectory filtering for vision based uav racing"):
  with members:
    - "deep drone racing learning agile flight in dynamic environments"
    - "a real time game theoretic planner for autonomous two player drone racing"
    - "following high level navigation instructions on a simulated quadcopter with imitation learning"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "teaching uavs to race end to end regression of agile controls in simulation"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "agile autonomous driving using end to end deep imitation learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-8 (induced by source paper "drone path following in gps denied environments using convolutional networks"):
  with members:
    - "active neural localization"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "relative camera pose estimation using convolutional neural networks"
    - "how hard is it to cross the room training recurrent neural networks to steer a uav"
    - "fast r cnn"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"

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)