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 "differentiable gaussian process motion planning"):
  Abstract: Modern trajectory optimization based approaches to motion planning are fast, easy to implement, and effective on a wide range of robotics tasks. However, trajectory optimization algorithms have parameters that are typically set in advance (and rarely discussed in detail). Setting these parameters properly can have a significant impact on the practical performance of the algorithm, sometimes making the difference between finding a feasible plan or failing at the task entirely. We propose a method for leveraging past experience to learn how to automatically adapt the parameters of Gaussian Process Motion Planning (GPMP) algorithms. Specifically, we propose a differentiable extension to the GPMP2 algorithm, so that it can be trained end-to-end from data. We perform several experiments that validate our algorithm and illustrate the benefits of our proposed learning-based approach to motion planning.

Includes members:
  Member "leveraging experience in lazy search":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "posterior sampling for anytime motion planning on graphs with expensive to evaluate edges")
  Member "differentiable mpc for end to end planning and control":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "objective mismatch in model based reinforcement learning")
  Member "neural ordinary differential equations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stochastic latent residual video prediction")
  Member "continuous time gaussian process motion planning via probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multimodal trajectory optimization for motion planning")
  Member "optnet differentiable optimization as a layer in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lossless compression of deep neural networks")
  Member "learning heuristic search via imitation":
    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 "prm rl long range robotic navigation tasks by combining reinforcement learning and sampling based planning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adjust planning strategies to accommodate reinforcement learning agents")
  Member "learning to learn by gradient descent by gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)