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 cognitive exploration through deep reinforcement learning for mobile robots"
  Abstract: Exploration in an unknown environment is the core functionality for mobile robots. Learning-based exploration methods, including convolutional neural networks, provide excellent strategies without human-designed logic for the feature extraction. But the conventional supervised learning algorithms cost lots of efforts on the labeling work of datasets inevitably. Scenes not included in the training set are mostly unrecognized either. We propose a deep reinforcement learning method for the exploration of mobile robots in an indoor environment with the depth information from an RGB-D sensor only. Based on the Deep Q-Network framework, the raw depth image is taken as the only input to estimate the Q values corresponding to all moving commands. The training of the network weights is end-to-end. In arbitrarily constructed simulation environments, we show that the robot can be quickly adapted to unfamiliar scenes without any man-made labeling. Besides, through analysis of receptive fields of feature representations, deep reinforcement learning motivates the convolutional networks to estimate the traversability of the scenes. The test results are compared with the exploration strategies separately based on deep learning or reinforcement learning. Even trained only in the simulated environment, experimental results in real-world environment demonstrate that the cognitive ability of robot controller is dramatically improved compared with the supervised method. We believe it is the first time that raw sensor information is used to build cognitive exploration strategy for mobile robots through end-to-end deep reinforcement learning.

Connected through hyperedge HE-1 (induced by source paper "evolving indoor navigational strategies using gated recurrent units in neat"):
  with members:
    - "a comparative study of bug algorithms for robot navigation"
    - "observe and look further achieving consistent performance on atari"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "control of memory active perception and action in minecraft"

Connected through hyperedge HE-2 (induced by source paper "building generalizable agents with a realistic and rich 3d environment"):
  with members:
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "grounding language for transfer in deep reinforcement learning"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "starcraft ii a new challenge for reinforcement learning"
    - "gated attention architectures for task oriented language grounding"
    - "neural map structured memory for deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "vision and language navigation interpreting visually grounded navigation instructions in real environments"):
  with members:
    - "gibson env real world perception for embodied agents"
    - "chalet cornell house agent learning environment"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "home a household multimodal environment"
    - "a dataset for developing and benchmarking active vision"
    - "gated attention architectures for task oriented language grounding"
    - "parlai a dialog research software platform"

Connected through hyperedge HE-4 (induced by source paper "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"):
  with members:
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "cad2rl real single image flight without a single real image"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"
    - "a survey of deep network solutions for learning control in robotics from reinforcement to imitation"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "deep reinforcement learning with successor features for navigation across similar environments"
    - "learning to navigate in complex environments"

Connected through hyperedge HE-5 (induced by source paper "towards continuous control of flippers for a multi terrain robot using deep reinforcement learning"):
  with members:
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
    - "asynchronous methods for deep reinforcement learning"
    - "learning values across many orders of magnitude"
    - "learning to navigate in complex environments"
    - "prioritized experience replay"
    - "continuous control with deep reinforcement learning"
    - "adam a method for stochastic optimization"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "deep reinforcement learning with successor features for navigation across similar environments"):
  with members:
    - "progressive neural networks"
    - "successor features for transfer in reinforcement learning"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "a survey of deep network solutions for learning control in robotics from reinforcement to imitation"
    - "prioritized experience replay"
    - "actor mimic deep multitask and transfer reinforcement learning"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"

Connected through hyperedge HE-7 (induced by source paper "learning to navigate in complex environments"):
  with members:
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"
    - "pixel recurrent neural networks"
    - "control of memory active perception and action in minecraft"
    - "recurrent reinforcement learning a hybrid approach"
    - "language understanding for text based games using deep reinforcement learning"
    - "massively parallel methods for deep reinforcement learning"
    - "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)