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 "towards navigation without precise localization weakly supervised learning of goal directed navigation cost map"):
  Abstract: Autonomous navigation based on precise localization has been widely developed in both academic research and practical applications. The high demand for localization accuracy has been essential for safe robot planing and navigation while it makes the current geometric solutions less robust to environmental changes. Recent research on end-to-end methods handle raw sensory data with forms of navigation instructions and directly output the command for robot control. However, the lack of intermediate semantics makes the system more rigid and unstable for practical use. To explore these issues, this paper proposes an innovate navigation framework based on the GPS-level localization, which takes the raw perception data with publicly accessible navigation maps to produce an intermediate navigation cost map that allows subsequent flexible motion planning. A deterministic conditional adversarial network is adopted in our method to generate visual goal-directed paths under diverse navigation conditions. The adversarial loss avoids the pixel-level annotation and enables a weakly supervised training strategy to implicitly learn both of the traffic semantics in image perceptions and the planning intentions in navigation instructions. The navigation cost map is then rendered from the goal-directed path and the concurrently collected laser data, indicating the way towards the destination. Comprehensive experiments have been conducted with a real vehicle running in our campus and the results have verified the robustness to localization error of the proposed navigation system.

Includes members:
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "variational end to end navigation and localization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "stereo vision based semantic 3d object and ego motion tracking for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "laser map aided visual inertial localization in changing environment":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "globally optimal consensus maximization for robust visual inertial localization in point and line map")
  Member "end to end learning of driving models with surround view cameras and route planners":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "semantic object prediction and spatial sound super resolution with binaural sounds")
  Member "ground edge based lidar localization without a reflectivity calibration for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ford multi av seasonal dataset")
  Member "intention net integrating planning and deep learning for goal directed autonomous navigation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "social navigation with human empowerment driven reinforcement learning")
  Member "3d ssd learning hierarchical features from rgb d images for amodal 3d object detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deepgoal learning to drive with driving intention from human control demonstration")

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)