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 "path planning in dynamic environments using generative rnns and monte carlo tree search"):
  Abstract: State of the art methods for robotic path planning in dynamic environments, such as crowds or traffic, rely on hand crafted motion models for agents. These models often do not reflect interactions of agents in real world scenarios. To overcome this limitation, this paper proposes an integrated path planning framework using generative Recurrent Neural Networks within a Monte Carlo Tree Search (MCTS). This approach uses a learnt model of social response to predict crowd dynamics during planning across the action space. This extends our recent work using generative RNNs to learn the relationship between planned robotic actions and the likely response of a crowd. We show that the proposed framework can considerably improve motion prediction accuracy during interactions, allowing more effective path planning. The performance of our method is compared in simulation with existing methods for collision avoidance in a crowd of pedestrians, demonstrating the ability to control future states of nearby individuals. We also conduct preliminary real world tests to validate the effectiveness of our method.

Includes members:
  Member "human motion trajectory prediction a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sequential forecasting of 100 000 points")
  Member "predicting responses to a robot s future motion using generative recurrent neural networks":
    also connected through other hyperedges:
      (none)
  Member "top view trajectories a pedestrian dataset of vehicle crowd interaction from controlled experiments and crowded campus":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "overview of tools supporting planning for automated driving")
  Member "an evaluation of trajectory prediction approaches and notes on the trajnet benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transformer networks for trajectory forecasting")
  Member "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to move with affordance maps")
  Member "motion planning among dynamic decision making agents with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "naturalistic driver intention and path prediction using recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "trajectory forecasts in unknown environments conditioned on grid based plans")
  Member "combining neural networks and tree search for task and motion planning in challenging environments":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a deep reinforcement learning based driving policy for autonomous road vehicles")

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)