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 "rrt combined with gvo for real time nonholonomic robot navigation in dynamic environment"):
  Abstract: Challenges persist in nonholonomic robot navigation for dynamic environments. This paper presents a framework for nonholonomic robot navigation in dynamic environment based on the model of Generalized Velocity Obstacles(GVO). The idea of velocity obstacles has been well studied and developed for obstacle avoidance since proposed in 1998. Though proved to be successful, most studies assume equations of motion to be linear, which limit the application to holonomic robots. In addition, more attention has been paid to the immediate reaction of robots while advance planning has always been ignored. By applying GVO model to differential drive robots and combining it with RRT*, we reduce the uncertainty of robot trajectory, thus further reduce the concerned range, and save both computation time and running time. By introducing uncertainty for the dynamic obstacles by Kalman filter, we reduce the risk of considering obstacles to uniformly move along a straight line and guarantee the safety. Special concern has been given to the path generation, including curvature check, making the generated path feasible for nonholonomic robots. We experimentally demonstrated the feasibility of the framework.

Includes members:
  Member "socially aware motion planning with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "social navigation with human empowerment driven reinforcement learning")
  Member "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "posterior sampling for anytime motion planning on graphs with expensive to evaluate edges")

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)