human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deepmotion learning to navigate like humans"):
  Abstract: We present a novel human-aware navigation approach, where the robot learns to mimic humans to navigate safely in crowds. The presented model referred to as DeepMoTIon, is trained with pedestrian surveillance data to predict human velocity. The robot processes LiDAR scans via the trained network to navigate to the target location. We conduct extensive experiments to assess the different components of our network and prove the necessity of each to imitate humans. Our experiments show that DeepMoTIon outperforms state-of-the-art in terms of human imitation and reaches the target on 100% of the test cases without breaching humans' safe distance.

Includes members:
  Member "lstm a search space odyssey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "learning generalized reactive policies using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transferable task execution from pixels through deep planning domain learning")
  Member "sociosense robot navigation amongst pedestrians with social and psychological constraints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "densecavoid real time navigation in dense crowds using anticipatory behaviors")
  Member "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "social wagdat interaction aware trajectory prediction via wasserstein graph double attention network")
  Member "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "densecavoid real time navigation in dense crowds using anticipatory behaviors")
  Member "socially aware motion planning with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "social navigation with human empowerment driven reinforcement learning")

Question: Please tell me which class the source paper 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)