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: "teaching a machine to read maps with deep reinforcement learning"
  Abstract: The ability to use a 2D map to navigate a complex 3D environment is quite remarkable, and even difficult for many humans. Localization and navigation is also an important problem in domains such as robotics, and has recently become a focus of the deep reinforcement learning community. In this paper we teach a reinforcement learning agent to read a map in order to find the shortest way out of a random maze it has never seen before. Our system combines several state-of-the-art methods such as A3C and incorporates novel elements such as a recurrent localization cell. Our agent learns to localize itself based on 3D first person images and an approximate orientation angle. The agent generalizes well to bigger mazes, showing that it learned useful localization and navigation capabilities.

Connected through hyperedge HE-1 (induced by source paper "on reward shaping for mobile robot navigation a reinforcement learning and slam based approach"):
  with members:
    - "deep reinforcement learning a brief survey"
    - "neural slam learning to explore with external memory"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
    - "deep reinforcement learning with successor features for navigation across similar environments"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "learning to navigate in cities without a map"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "semi parametric topological memory for navigation"
    - "a critical investigation of deep reinforcement learning for navigation"
    - "understanding grounded language learning agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "active neural localization"
    - "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 "saferoute learning to navigate streets safely in an urban environment"):
  with members:
    - "semi parametric topological memory for navigation"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"
    - "learning to navigate in cities without a map"
    - "node2vec scalable feature learning for networks"
    - "reinforcement learning neural turing machines revised"
    - "adam a method for stochastic optimization"
    - "the shortest path to happiness recommending beautiful quiet and happy routes in the city"

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)