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: "curiosity driven exploration for mapless navigation with deep reinforcement learning"
  Abstract: This paper investigates exploration strategies of Deep Reinforcement Learning (DRL) methods to learn navigation policies for mobile robots. In particular, we augment the normal external reward for training DRL algorithms with intrinsic reward signals measured by curiosity. We test our approach in a mapless navigation setting, where the autonomous agent is required to navigate without the occupancy map of the environment, to targets whose relative locations can be easily acquired through low-cost solutions (e.g., visible light localization, Wi-Fi signal localization). We validate that the intrinsic motivation is crucial for improving DRL performance in tasks with challenging exploration requirements. Our experimental results show that our proposed method is able to more effectively learn navigation policies, and has better generalization capabilities in previously unseen environments. A video of our experimental results can be found at this https URL

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 "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"
    - "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"
    - "towards monocular vision based obstacle avoidance through deep reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-3 (induced by source paper "mapless navigation among dynamics with social safety awareness a reinforcement learning approach from 2d laser scans"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "crowdmove autonomous mapless navigation in crowded scenarios"
    - "intention net integrating planning and deep learning for goal directed autonomous navigation"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "learning end to end multimodal sensor policies for autonomous navigation"

Connected through hyperedge HE-4 (induced by source paper "using rgb image as visual input for mapless robot navigation"):
  with members:
    - "composable deep reinforcement learning for robotic manipulation"
    - "on evaluation of embodied navigation agents"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "learning to navigate in cities without a map"
    - "curiosity driven exploration by self supervised prediction"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
    - "generative adversarial imitation learning"
    - "continuous deep q learning with model based acceleration"

Connected through hyperedge HE-5 (induced by source paper "vr goggles for robots real to sim domain adaptation for visual control"):
  with members:
    - "artistic style transfer for videos and spherical images"
    - "transfer learning from synthetic to real images using variational autoencoders for precise position detection"
    - "sem gan semantically consistent image to image translation"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "cycada cycle consistent adversarial domain adaptation"
    - "carla an open urban driving simulator"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

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)