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 "motion planning networks"):
  Abstract: Fast and efficient motion planning algorithms are crucial for many state-of-the-art robotics applications such as self-driving cars. Existing motion planning methods become ineffective as their computational complexity increases exponentially with the dimensionality of the motion planning problem. To address this issue, we present Motion Planning Networks (MPNet), a neural network-based novel planning algorithm. The proposed method encodes the given workspaces directly from a point cloud measurement and generates the end-to-end collision-free paths for the given start and goal configurations. We evaluate MPNet on various 2D and 3D environments including the planning of a 7 DOF Baxter robot manipulator. The results show that MPNet is not only consistently computationally efficient in all environments but also generalizes to completely unseen environments. The results also show that the computation time of MPNet consistently remains less than 1 second in all presented experiments, which is significantly lower than existing state-of-the-art motion planning algorithms.

Includes members:
  Member "learning based proxy collision detection for robot motion planning applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "autonomous navigation in unknown environments using sparse kernel based occupancy mapping")
  Member "potentially guided bidirectionalized rrt for fast optimal path planning in cluttered environments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "motion planning networks bridging the gap between learning based and classical motion planners")
  Member "deeply informed neural sampling for robot motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient exploration in constrained environments with goal oriented reference path")
  Member "fastron an online learning based model and active learning strategy for proxy collision detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "autonomous navigation in unknown environments using sparse kernel based occupancy mapping")
  Member "learning sampling distributions for robot motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "accelerating cooperative planning for automated vehicles with learned heuristics and monte carlo tree search")
  Member "potential functions based sampling heuristic for optimal path planning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "motion planning networks bridging the gap between learning based and classical motion planners")
  Member "end to end learning for self driving cars":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "overcoming catastrophic forgetting in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on contextual embeddings")

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)