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 "trajectory optimization for coordinated human robot collaboration"):
  Abstract: Effective human-robot collaboration requires informed anticipation. The robot must simultaneously anticipate what the human will do and react both instantaneously and fluidly when its predictions are wrong. Even more, the robot must plan its own actions in a way that accounts for the human predictions but also with the knowledge that the human's own behavior will change based on what the robot does. This back-and-forth game of prediction and planning is extremely difficult to model well using standard techniques. In this work, we exploit the duality between behavior prediction and control explored in the Inverse Optimal Control (IOC) literature to design a novel Model Predictive Control (MPC) algorithm that simultaneously plans the robot's behavior and predicts the human's behavior in a joint optimal control model. In the process, we develop a novel technique for bridging finite-horizon motion optimizers to the problem of spatially consistent continuous optimization using explicit sparse reward terms, i.e., negative cost. We demonstrate the framework on a collection of cooperative human-robot handover experiments in both simulation and with a real-world handover scenario.

Includes members:
  Member "towards a robust aerial cinematography platform localizing and tracking moving targets in unstructured environments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning visuomotor policies for aerial navigation using cross modal representations")
  Member "riemannian motion policies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "camera to robot pose estimation from a single image")
  Member "rmpflow a computational graph for automatic motion policy generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "camera to robot pose estimation from a single image")
  Member "sparse gaussian processes for continuous time trajectory estimation on matrix lie groups":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "steap simultaneous trajectory estimation and planning")
  Member "on the fundamental importance of gauss newton in motion optimization":
    also connected through other hyperedges:
      (none)
  Member "newton methods for k order markov constrained motion problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "prediction of human full body movements with motion optimization and recurrent neural networks")
  Member "continuous inverse optimal control with locally optimal examples":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial imitation learning with neural networks global optimality and convergence rate")

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)