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: "crowdmove autonomous mapless navigation in crowded scenarios"
  Abstract: Navigation is an essential capability for mobile robots. In this paper, we propose a generalized yet effective 3M (i.e., multi-robot, multi-scenario, and multi-stage) training framework. We optimize a mapless navigation policy with a robust policy gradient algorithm. Our method enables different types of mobile platforms to navigate safely in complex and highly dynamic environments, such as pedestrian crowds. To demonstrate the superiority of our method, we test our methods with four kinds of mobile platforms in four scenarios. Videos are available at this https URL.

Connected through hyperedge HE-1 (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"
    - "curiosity driven exploration for mapless navigation with deep reinforcement learning"
    - "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-2 (induced by source paper "the emotionally intelligent robot improving social navigation in crowded environments"):
  with members:
    - "deep learning for human affect recognition insights and new developments"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "sociosense robot navigation amongst pedestrians with social and psychological constraints"
    - "social attention modeling attention in human crowds"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "uncertainty aware reinforcement learning for collision avoidance"
    - "xception deep learning with depthwise separable convolutions"
    - "real time crowd tracking using parameter optimized mixture of motion models"

Connected through hyperedge HE-3 (induced by source paper "lets drive driving in a crowd by learning from tree search"):
  with members:
    - "hyp despot a hybrid parallel algorithm for online planning under uncertainty"
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "gated path planning networks"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "thinking fast and slow with deep learning and tree search"
    - "socially aware motion planning with deep reinforcement learning"
    - "despot online pomdp planning with regularization"
    - "value iteration networks"

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)