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: "one shot reinforcement learning for robot navigation with interactive replay"
  Abstract: Recently, model-free reinforcement learning algorithms have been shown to solve challenging problems by learning from extensive interaction with the environment. A significant issue with transferring this success to the robotics domain is that interaction with the real world is costly, but training on limited experience is prone to overfitting. We present a method for learning to navigate, to a fixed goal and in a known environment, on a mobile robot. The robot leverages an interactive world model built from a single traversal of the environment, a pre-trained visual feature encoder, and stochastic environmental augmentation, to demonstrate successful zero-shot transfer under real-world environmental variations without fine-tuning.

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 "cross view policy learning for street navigation"):
  with members:
    - "learning to follow directions in street view"
    - "the streetlearn environment and dataset"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "global pose estimation with an attention based recurrent network"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "the adobeindoornav dataset towards deep reinforcement learning based real world indoor robot visual navigation"
    - "unsupervised predictive memory in a goal directed agent"

Connected through hyperedge HE-3 (induced by source paper "long range neural navigation policies for the real world"):
  with members:
    - "learning navigation behaviors end to end with autorl"
    - "gibson env real world perception for embodied agents"
    - "semi parametric topological memory for navigation"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "distributed prioritized experience replay"
    - "learning to navigate in cities without a map"
    - "learning deployable navigation policies at kilometer scale from a single traversal"
    - "minos multimodal indoor simulator for navigation in complex environments"

Connected through hyperedge HE-4 (induced by source paper "learning your way without map or compass panoramic target driven visual navigation"):
  with members:
    - "long range indoor navigation with prm rl"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "habitat a platform for embodied ai research"
    - "learning navigation behaviors end to end with autorl"
    - "gibson env real world perception for embodied agents"
    - "on evaluation of embodied navigation agents"
    - "learning to navigate in cities without a map"
    - "rednet residual encoder decoder network for indoor rgb d semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "evaluating task agnostic exploration for fixed batch learning of arbitrary future tasks"):
  with members:
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "exploration by random network distillation"
    - "model ensemble trust region policy optimization"
    - "off policy deep reinforcement learning without exploration"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "large scale study of curiosity driven learning"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-6 (induced by source paper "topological navigation graph"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "semi parametric topological memory for navigation"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "learning to drive from simulation without real world labels"
    - "deep object centric policies for autonomous driving"
    - "on offline evaluation of vision based driving models"
    - "learning deployable navigation policies at kilometer scale from a single traversal"

Connected through hyperedge HE-7 (induced by source paper "vision based navigation using deep reinforcement learning"):
  with members:
    - "building generalizable agents with a realistic and rich 3d environment"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "ai2 thor an interactive 3d environment for visual ai"
    - "semantic scene completion from a single depth image"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "learning to navigate in complex environments"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "adaptive leader follower formation control and obstacle avoidance via deep reinforcement learning"):
  with members:
    - "deep global relative networks for end to end 6 dof visual localization and odometry"
    - "an introduction to deep reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "learning montezuma s revenge from a single demonstration"
    - "learning to navigate in cities without a map"
    - "learning deployable navigation policies at kilometer scale from a single traversal"
    - "modelling uncertainty in deep learning for camera relocalization"
    - "continuous control with deep reinforcement 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.AI(Artificial Intelligence)