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: "towards monocular vision based obstacle avoidance through deep reinforcement learning"
  Abstract: Obstacle avoidance is a fundamental requirement for autonomous robots which operate in, and interact with, the real world. When perception is limited to monocular vision avoiding collision becomes significantly more challenging due to the lack of 3D information. Conventional path planners for obstacle avoidance require tuning a number of parameters and do not have the ability to directly benefit from large datasets and continuous use. In this paper, a dueling architecture based deep double-Q network (D3QN) is proposed for obstacle avoidance, using only monocular RGB vision. Based on the dueling and double-Q mechanisms, D3QN can efficiently learn how to avoid obstacles in a simulator even with very noisy depth information predicted from RGB image. Extensive experiments show that D3QN enables twofold acceleration on learning compared with a normal deep Q network and the models trained solely in virtual environments can be directly transferred to real robots, generalizing well to various new environments with previously unseen dynamic objects.

Connected through hyperedge HE-1 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview"):
  with members:
    - "selective sensor fusion for neural visual inertial odometry"
    - "learning monocular depth estimation infusing traditional stereo knowledge"
    - "deep direct visual odometry"
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "recurrent neural network for un supervised learning of monocular videovisual odometry and depth"
    - "multi frame gan image enhancement for stereo visual odometry in low light"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "sganvo unsupervised deep visual odometry and depth estimation with stacked generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "densecavoid real time navigation in dense crowds using anticipatory behaviors"):
  with members:
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "yolov3 an incremental improvement"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "fully distributed multi robot collision avoidance via deep reinforcement learning for safe and efficient navigation in complex scenarios"

Connected through hyperedge HE-3 (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"
    - "obstacle avoidance through deep networks based intermediate perception"
    - "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-4 (induced by source paper "map less navigation a single drl based controller for robots with varied dimensions"):
  with members:
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "soft actor critic algorithms and applications"
    - "learning with stochastic guidance for navigation"
    - "addressing function approximation error in actor critic methods"
    - "curiosity driven exploration for mapless navigation with deep reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "learning with training wheels speeding up training with a simple controller for deep reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

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 "improved reinforcement learning through imitation learning pretraining towards image based autonomous driving"):
  with members:
    - "learning accurate comfortable and human like driving"
    - "drone path following in gps denied environments using convolutional networks"
    - "bam bottleneck attention module"
    - "learning transferable uav for forest visual perception"
    - "cbam convolutional block attention module"
    - "squeeze and excitation networks"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "cad2rl real single image flight without a single real image"

Connected through hyperedge HE-7 (induced by source paper "end to end decentralized multi robot navigation in unknown complex environments via deep reinforcement learning"):
  with members:
    - "self supervised deep reinforcement learning with generalized computation graphs for robot navigation"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "socially aware motion planning with deep reinforcement learning"
    - "proximal policy optimization algorithms"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (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"
    - "obstacle avoidance through deep networks based intermediate perception"
    - "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"

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)