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: "obstacle avoidance through deep networks based intermediate perception"
  Abstract: Obstacle avoidance from monocular images is a challenging problem for robots. Though multi-view structure-from-motion could build 3D maps, it is not robust in textureless environments. Some learning based methods exploit human demonstration to predict a steering command directly from a single image. However, this method is usually biased towards certain tasks or demonstration scenarios and also biased by human understanding. In this paper, we propose a new method to predict a trajectory from images. We train our system on more diverse NYUv2 dataset. The ground truth trajectory is computed from the designed cost functions automatically. The Convolutional Neural Network perception is divided into two stages: first, predict depth map and surface normal from RGB images, which are two important geometric properties related to 3D obstacle representation. Second, predict the trajectory from the depth and normal. Results show that our intermediate perception increases the accuracy by 20% than the direct prediction. Our model generalizes well to other public indoor datasets and is also demonstrated for robot flights in simulation and experiments.

Connected through hyperedge HE-1 (induced by source paper "danger aware adaptive composition of drl agents for self navigation"):
  with members:
    - "multi advisor reinforcement learning"
    - "learning with training wheels speeding up training with a simple controller for deep reinforcement learning"
    - "utility decomposition with deep corrections for scalable planning under uncertainty"
    - "towards monocular vision based obstacle avoidance through deep reinforcement learning"
    - "feudal networks for hierarchical reinforcement learning"
    - "socially aware motion planning with deep reinforcement learning"
    - "uncertainty aware reinforcement learning for collision avoidance"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"

Connected through hyperedge HE-2 (induced by source paper "an interactive indoor drone assistant"):
  with members:
    - "multi task learning to improve natural language understanding"
    - "ultra low power deep learning powered autonomous nano drones"
    - "skimap an efficient mapping framework for robot navigation"
    - "unsupervised monocular depth estimation with left right consistency"
    - "deeper depth prediction with fully convolutional residual networks"
    - "deep neural network for real time autonomous indoor navigation"
    - "neural machine translation of rare words with subword units"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "learning with training wheels speeding up training with a simple controller for deep reinforcement learning"):
  with members:
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "towards monocular vision based obstacle avoidance through deep reinforcement learning"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "learning to fly by crashing"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
    - "one shot imitation learning"

Connected through hyperedge HE-4 (induced by source paper "memory based deep reinforcement learning for obstacle avoidance in uav with limited environment knowledge"):
  with members:
    - "j mod 2 joint monocular obstacle detection and depth estimation"
    - "began boundary equilibrium generative adversarial networks"
    - "unsupervised learning of depth and ego motion from video"
    - "regret minimization for partially observable deep reinforcement learning"
    - "semi supervised deep learning for monocular depth map prediction"
    - "fast robust monocular depth estimation for obstacle detection with fully convolutional networks"
    - "temporal attention gated model for robust sequence classification"
    - "deeper depth prediction with fully convolutional residual networks"

Connected through hyperedge HE-5 (induced by source paper "j mod 2 joint monocular obstacle detection and depth estimation"):
  with members:
    - "analyzing modular cnn architectures for joint depth prediction and semantic segmentation"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "3d bounding box estimation using deep learning and geometry"
    - "robust monocular flight in cluttered outdoor environments"
    - "you only look once unified real time object detection"
    - "very deep convolutional networks for large scale image recognition"
    - "depth map prediction from a single image using a multi scale deep network"

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)